Poster Presented at XXXVII Sunbelt Conference
of The International Network For Social Network Analysis (INSNA)
May 30th, 2017 – June 4th, 2017 Beijing, China
Benchmarking the Privacy-Preserving People SearchDaqing He
This document discusses benchmarking privacy-preserving people search. Social match is important in people search because tighter social similarity makes it easier to connect people. However, privacy is a big concern with people search using social networks due to users opting out of sharing information or providing incomplete data. The research aims to obtain a privacy-preserving social network by simulating with public coauthor networks. Both global and local network features are important for people search performance, and accounting for privacy concerns in these features can significantly impact search results. The local network feature, relating to direct connections, has more influence than global features relating to whole network propagation.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
This document provides an overview of social network analysis, including what social networks are, what can be learned from analyzing social networks, and how social network analysis can be performed. Some key findings that can be uncovered include the six degrees of separation principle, the 80-20 rule of social popularity where a minority of nodes have most connections, how to identify influential nodes, and how to group similar nodes into communities. Various metrics and models are described for analyzing features like path lengths, degree distributions, ranking nodes, measuring community structure, and more. Examples of social network analysis are also provided.
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then covers key concepts in social network analysis including nodes, edges, metrics like centrality and density. NodeXL is introduced as a tool for visualizing and analyzing social networks from data collected from sources like personal emails, Twitter, forums and YouTube. Examples of social network analyses using NodeXL are provided such as mapping corporate email communication and identifying influencers on Twitter.
This document outlines topics in social network analysis presented by Suman Banerjee of IIT Kharagpur. It introduces basics of modeling social networks as graphs and outlines several research issues including community detection, link prediction, opinion dynamics, influence propagation, and stability analysis. It also lists some tools, journals, conferences, and top researchers in the field of social network analysis.
Community analysis using graph representation learning on social networksMarco Brambilla
This document proposes a method to analyze communities on social networks using graph representation learning. It involves collecting data on brands and followers from Instagram, constructing graphs to model interactions, extracting embeddings using node2vec, classifying users, and clustering communities. Experiments on an Italian fashion brand found embeddings from reduced graphs performed well in classification. Clustering identified sub-communities validated by domain experts as related to professionals, holidays, and regular users. The method effectively analyzed social network communities through network modeling and representation learning.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Benchmarking the Privacy-Preserving People SearchDaqing He
This document discusses benchmarking privacy-preserving people search. Social match is important in people search because tighter social similarity makes it easier to connect people. However, privacy is a big concern with people search using social networks due to users opting out of sharing information or providing incomplete data. The research aims to obtain a privacy-preserving social network by simulating with public coauthor networks. Both global and local network features are important for people search performance, and accounting for privacy concerns in these features can significantly impact search results. The local network feature, relating to direct connections, has more influence than global features relating to whole network propagation.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
This document provides an overview of social network analysis, including what social networks are, what can be learned from analyzing social networks, and how social network analysis can be performed. Some key findings that can be uncovered include the six degrees of separation principle, the 80-20 rule of social popularity where a minority of nodes have most connections, how to identify influential nodes, and how to group similar nodes into communities. Various metrics and models are described for analyzing features like path lengths, degree distributions, ranking nodes, measuring community structure, and more. Examples of social network analysis are also provided.
How to conduct a social network analysis: A tool for empowering teams and wor...Jeromy Anglim
Slides and details available at: http://jeromyanglim.blogspot.com/2009/10/how-to-conduct-social-network-analysis.html
A talk on using social network analysis as a team development tool.
In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.
This document discusses analyzing social media networks using NodeXL. It defines social media and lists common types. It then covers key concepts in social network analysis including nodes, edges, metrics like centrality and density. NodeXL is introduced as a tool for visualizing and analyzing social networks from data collected from sources like personal emails, Twitter, forums and YouTube. Examples of social network analyses using NodeXL are provided such as mapping corporate email communication and identifying influencers on Twitter.
This document outlines topics in social network analysis presented by Suman Banerjee of IIT Kharagpur. It introduces basics of modeling social networks as graphs and outlines several research issues including community detection, link prediction, opinion dynamics, influence propagation, and stability analysis. It also lists some tools, journals, conferences, and top researchers in the field of social network analysis.
Community analysis using graph representation learning on social networksMarco Brambilla
This document proposes a method to analyze communities on social networks using graph representation learning. It involves collecting data on brands and followers from Instagram, constructing graphs to model interactions, extracting embeddings using node2vec, classifying users, and clustering communities. Experiments on an Italian fashion brand found embeddings from reduced graphs performed well in classification. Clustering identified sub-communities validated by domain experts as related to professionals, holidays, and regular users. The method effectively analyzed social network communities through network modeling and representation learning.
An introduction in the world of Social Network Analysis and a view on how this may help learning networks. History, data collection and several analysis techniques are shown.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
A comparative study of social network analysis toolsDavid Combe
This document compares several social network analysis tools based on their functionalities and benchmarks them using sample datasets. It finds that Pajek, Gephi, igraph, and NetworkX are mature tools that handle network representation, visualization, characterization with indicators, and community detection well. Gephi is interactive but community detection is experimental. NetworkX is attribute-friendly and handles large networks but lacks visualization. Igraph is optimized for clustering but not custom attributes. The best tool depends on the specific analysis needs.
This document discusses different types of network experiments and interventions. It describes (1) using roommate assignments to make social connections exogenous, assessing peer effects on outcomes like GPA. It also discusses (2) natural experiments that manipulate exposure over existing networks, like popularity or voter turnout. Finally, it outlines (3) different types of network interventions, including targeting influential individuals, segmenting groups, inducing new connections, and altering network structure. The conclusion is that evidence from these experiments shows peer influence is real and we can now focus on how to leverage networks most effectively.
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
This document discusses social network analysis (SNA), a tool used to analyze social relationships and networks. SNA elicits, analyzes, and visualizes how actors interact and resources move through a network. It represents actors as nodes and their relationships as ties. The document provides examples of SNA's application in education, health, and agriculture. It outlines the process of conducting SNA through workshops or surveys to collect node attribute and tie/link data, which is then analyzed using software to visualize the network. The document suggests opportunities to further develop SNA, such as presenting networks back to communities and measuring social capital.
The document discusses network diffusion and peer influence. It begins by defining diffusion and compartment models used to model disease spread. It then discusses how network structure, including topology, timing of connections, and structural transmission, can impact diffusion. Simulation is proposed to test how network features like distance, clustering, redundancy, and high-degree nodes influence spread. The relationships between contact networks, exposure networks based on timing, and actual transmission networks are also introduced.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
Mining and analyzing social media part 2 - hicss47 tutorial - dave kingDave King
This document provides an overview and introduction to social network analysis metrics and techniques for analyzing social media data. It discusses common social network analysis concepts like degrees of separation, centrality measures to identify influential users, and cohesion measures to understand how well connected a network is. It also presents examples analyzing Facebook networks and techniques for identifying cohesive subgroups within large social networks. The document demonstrates how social network analysis can be used to systematically study relationships and information flow within social systems.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
This document discusses social network analysis and its applications. It defines a social network as being composed of actors (people or groups) connected by social relationships. Social network analysis can be used to map these relationships visually using sociograms, understand information flow and community structure, and identify influential actors through metrics like centrality and betweenness. Tools like NodeXL and Gephi enable network extraction, visualization, and analysis to glean strategic insights from social networks.
This document discusses using social network analysis to design and implement behavior change interventions. It begins by outlining key network concepts like diffusion of innovations and mathematical models of diffusion. It then discusses how social networks influence behaviors through concepts like network exposure, tie strength, and thresholds. The document concludes by describing how to use social network analysis at different stages of intervention including needs assessment, program design, implementation, and monitoring through approaches like network ethnography, identifying opinion leaders, and using network diagnostics.
Who creates trends in online social mediaAmir Razmjou
The document discusses and compares two paradigms for how trends spread in online social media: the influential hypothesis and the crowd hypothesis. The influential hypothesis suggests that a small number of "opinion leaders" drive the adoption of new trends, acting as intermediaries between the media and the public. However, the document argues this view is outdated and does not apply well to today's internet era. Instead, evidence shows the crowd's early participation in spreading ideas can lead to widespread diffusion, rather than domination by influential elites. Analysis of slang word adoption over time supports the crowd hypothesis, finding ordinary users with around 150-300 followers play a key role in trends going viral.
This document discusses analyzing a social network on Facebook. It begins by introducing the project team and advisor. It then provides definitions of key social network analysis (SNA) concepts like nodes, edges, degree centrality, betweenness centrality, and clustering coefficient. The document outlines analyzing a sample Facebook network to identify high degree nodes and understand their behavior. The goal is to explore how Facebook uses basic SNA elements to recommend potential friends.
This document summarizes a study exploring patterns of self-organization in the Greek political blogosphere and discussion of eParticipation topics. The researchers located 101 Greek political blogs and analyzed their interlinkages through blogrolls and discussion topics. Through network and content analysis, they identified 4 central clusters of blogs. Clusters showed similarities between their interlinkages and coverage of topics like information provision, campaigning, and concern creation. The study found evidence that centrally connected blog groups discuss similar eParticipation issues broadly, indicating organization around discussion areas rather than polarization.
This document provides an overview of social network analysis (SNA) including concepts, methods, and applications. It begins with background on how SNA originated from social science and network analysis/graph theory. Key concepts discussed include representing social networks as graphs, identifying strong and weak ties, central nodes, and network cohesion. Practical applications of SNA are also outlined, such as in business, law enforcement, and social media sites. The document concludes by recommending when and why to use SNA.
This document summarizes a lecture on social information retrieval. It discusses social search, which takes social networks into account. One study examined questions people ask their social networks on Facebook and Twitter. It found questions were short, directed to "anyone", and about acceptable topics like relationships. Fast responses were considered helpful. Centrality measures like degree, closeness, and betweenness are used to determine important nodes in social networks. Strong and weak ties play different roles in information diffusion. Tie strength can be estimated using topology, neighborhood overlap, and profile/interaction data.
This document summarizes research on social contagion using social network data. It describes analyzing the Framingham Heart Study network data (FHS-Net) of over 12,000 individuals connected through family, friendship, coworkers and neighbors over 30+ years. The researchers have also analyzed other datasets like the National Longitudinal Study of Adolescent Health. Their research has found evidence that behaviors, states and traits like obesity, smoking, happiness and depression show clustering within social networks, suggesting the spread of influence through network ties. The researchers acknowledge limitations of current methods and hope to help develop new statistical approaches for analyzing network data.
This document summarizes a study that used a stochastic actor-oriented model to analyze data from a randomized controlled trial in Tanzania. The trial examined how social networks influenced HIV testing rates among young men. Survey data on men's friendship networks and HIV testing behaviors were collected at three time points. The model estimated the effects of descriptive and injunctive social norms within friendship networks and across camps on changes in men's HIV testing from the second to third time points, while accounting for selection effects. The results provide insight into how social influence spreads within networks and impacts health behaviors over time.
A method to evaluate the reliability of social media data for social network ...Derek Weber
In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
Co-authors: Mehwish Nasim (Data61 / CSIRO), Lewis Mitchell (University of Adelaide), Lucia Falzon (University of Melbourne / DST Group)
Comparison of Online Social Relations in terms of Volume vs. Interaction: A C...Haewoon Kwak
This document analyzes user interactions and social relationships in Cyworld, a large online social network in Korea. It compares the declared online friendships to actual interaction data from billions of guestbook messages over 2.5 years. Key findings include:
1) The network exhibits heterogeneous relationships and assortative mixing, with a few highly connected users. Interactions between friends are highly reciprocal but disparities exist based on number of friends.
2) Microscopic analysis found that users with fewer than 200 friends have a dominant interaction partner, while those with over 1,000 friends interact more evenly. Triadic relationships are common.
3) Additional observations showed that more online friends correlated with more user activity, up to around
Understanding Collaboration in Fluid Organizations, a Proximity ApproachDawn Foster
Dawn Foster, Guido Conaldi, Riccardo De Vita
University of Greenwich
Centre for Business Network Analysis
http://www.gre.ac.uk/business/research/centres/cbna/home
Presented at the Third European Conference on Social Networks (EUSN) Mainz, Germany on 27 September 2017
This study investigates collaboration in an open source software community using proximity theory as the theoretical lens with social network analysis and modeling of activities over time to predict collaboration.
Actors in this study are part of the Linux kernel community where they collaborate on one or more sub-projects using mailing lists as the primary method of collaboration. Collaboration occurs in real-time between actors that contribute to multiple sub-projects, work for firms that pay them to contribute to the Linux kernel, and are working virtually from locations across the globe. This complex setting can be better understood by using several dimensions of proximity: organizational, cognitive, institutional, social, and geographical. Collaboration is analysed using data from source code contributions and mailing list participation.
Open source software is developed in the open where anyone can view the source code and anyone with the knowledge to do so can contribute to the project. With no central group responsible for coordination of tasks, collaboration on the development of this software is emergent. Because people from around the world work on these projects together using online tools with publicly accessible interactions between people, it is a relevant setting for using social network analysis to understand and model network relationships.
Multilevel Collaboration between Software Developers and the Impact of Proxim...Dawn Foster
This document summarizes early work on analyzing multilevel collaboration networks between software developers. The researchers aim to understand how proximity impacts collaboration within fluid organizations, using the Linux kernel open source project. Preliminary results from a relational event model found that developers were less likely to collaborate with others from the same firm or in different time zones. However, the model had limitations due to its small scale and was not yet able to fully represent collaboration across multiple levels. The researchers seek feedback on better approaches for multilevel analysis and modeling large collaborative networks.
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
A comparative study of social network analysis toolsDavid Combe
This document compares several social network analysis tools based on their functionalities and benchmarks them using sample datasets. It finds that Pajek, Gephi, igraph, and NetworkX are mature tools that handle network representation, visualization, characterization with indicators, and community detection well. Gephi is interactive but community detection is experimental. NetworkX is attribute-friendly and handles large networks but lacks visualization. Igraph is optimized for clustering but not custom attributes. The best tool depends on the specific analysis needs.
This document discusses different types of network experiments and interventions. It describes (1) using roommate assignments to make social connections exogenous, assessing peer effects on outcomes like GPA. It also discusses (2) natural experiments that manipulate exposure over existing networks, like popularity or voter turnout. Finally, it outlines (3) different types of network interventions, including targeting influential individuals, segmenting groups, inducing new connections, and altering network structure. The conclusion is that evidence from these experiments shows peer influence is real and we can now focus on how to leverage networks most effectively.
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
This document discusses social network analysis (SNA), a tool used to analyze social relationships and networks. SNA elicits, analyzes, and visualizes how actors interact and resources move through a network. It represents actors as nodes and their relationships as ties. The document provides examples of SNA's application in education, health, and agriculture. It outlines the process of conducting SNA through workshops or surveys to collect node attribute and tie/link data, which is then analyzed using software to visualize the network. The document suggests opportunities to further develop SNA, such as presenting networks back to communities and measuring social capital.
The document discusses network diffusion and peer influence. It begins by defining diffusion and compartment models used to model disease spread. It then discusses how network structure, including topology, timing of connections, and structural transmission, can impact diffusion. Simulation is proposed to test how network features like distance, clustering, redundancy, and high-degree nodes influence spread. The relationships between contact networks, exposure networks based on timing, and actual transmission networks are also introduced.
The document discusses different types of network experiments and interventions. It describes (1) assigning roommates randomly to manipulate networks and assess peer effects, (2) using natural experiments to manipulate exposure over existing networks, and (3) interventions that use networks to affect change. Specifically, it covers exogenous network experiments that randomly assign relationships, issues with experimental assignment, and four types of interventions: targeting individuals, segmentation, induction, and alteration.
Mining and analyzing social media part 2 - hicss47 tutorial - dave kingDave King
This document provides an overview and introduction to social network analysis metrics and techniques for analyzing social media data. It discusses common social network analysis concepts like degrees of separation, centrality measures to identify influential users, and cohesion measures to understand how well connected a network is. It also presents examples analyzing Facebook networks and techniques for identifying cohesive subgroups within large social networks. The document demonstrates how social network analysis can be used to systematically study relationships and information flow within social systems.
The document discusses network diffusion and peer influence. It covers compartmental models of diffusion, how network structure affects diffusion through factors like distance, clustering, and highly connected nodes. Simulation studies show networks with shorter path distances, more independent paths between nodes, and higher clustering coefficients diffuse ideas and behaviors more quickly. The regression analysis finds these network structural characteristics strongly predict a network's relative diffusion ratio compared to random networks.
This document discusses social network analysis and its applications. It defines a social network as being composed of actors (people or groups) connected by social relationships. Social network analysis can be used to map these relationships visually using sociograms, understand information flow and community structure, and identify influential actors through metrics like centrality and betweenness. Tools like NodeXL and Gephi enable network extraction, visualization, and analysis to glean strategic insights from social networks.
This document discusses using social network analysis to design and implement behavior change interventions. It begins by outlining key network concepts like diffusion of innovations and mathematical models of diffusion. It then discusses how social networks influence behaviors through concepts like network exposure, tie strength, and thresholds. The document concludes by describing how to use social network analysis at different stages of intervention including needs assessment, program design, implementation, and monitoring through approaches like network ethnography, identifying opinion leaders, and using network diagnostics.
Who creates trends in online social mediaAmir Razmjou
The document discusses and compares two paradigms for how trends spread in online social media: the influential hypothesis and the crowd hypothesis. The influential hypothesis suggests that a small number of "opinion leaders" drive the adoption of new trends, acting as intermediaries between the media and the public. However, the document argues this view is outdated and does not apply well to today's internet era. Instead, evidence shows the crowd's early participation in spreading ideas can lead to widespread diffusion, rather than domination by influential elites. Analysis of slang word adoption over time supports the crowd hypothesis, finding ordinary users with around 150-300 followers play a key role in trends going viral.
This document discusses analyzing a social network on Facebook. It begins by introducing the project team and advisor. It then provides definitions of key social network analysis (SNA) concepts like nodes, edges, degree centrality, betweenness centrality, and clustering coefficient. The document outlines analyzing a sample Facebook network to identify high degree nodes and understand their behavior. The goal is to explore how Facebook uses basic SNA elements to recommend potential friends.
This document summarizes a study exploring patterns of self-organization in the Greek political blogosphere and discussion of eParticipation topics. The researchers located 101 Greek political blogs and analyzed their interlinkages through blogrolls and discussion topics. Through network and content analysis, they identified 4 central clusters of blogs. Clusters showed similarities between their interlinkages and coverage of topics like information provision, campaigning, and concern creation. The study found evidence that centrally connected blog groups discuss similar eParticipation issues broadly, indicating organization around discussion areas rather than polarization.
This document provides an overview of social network analysis (SNA) including concepts, methods, and applications. It begins with background on how SNA originated from social science and network analysis/graph theory. Key concepts discussed include representing social networks as graphs, identifying strong and weak ties, central nodes, and network cohesion. Practical applications of SNA are also outlined, such as in business, law enforcement, and social media sites. The document concludes by recommending when and why to use SNA.
This document summarizes a lecture on social information retrieval. It discusses social search, which takes social networks into account. One study examined questions people ask their social networks on Facebook and Twitter. It found questions were short, directed to "anyone", and about acceptable topics like relationships. Fast responses were considered helpful. Centrality measures like degree, closeness, and betweenness are used to determine important nodes in social networks. Strong and weak ties play different roles in information diffusion. Tie strength can be estimated using topology, neighborhood overlap, and profile/interaction data.
This document summarizes research on social contagion using social network data. It describes analyzing the Framingham Heart Study network data (FHS-Net) of over 12,000 individuals connected through family, friendship, coworkers and neighbors over 30+ years. The researchers have also analyzed other datasets like the National Longitudinal Study of Adolescent Health. Their research has found evidence that behaviors, states and traits like obesity, smoking, happiness and depression show clustering within social networks, suggesting the spread of influence through network ties. The researchers acknowledge limitations of current methods and hope to help develop new statistical approaches for analyzing network data.
This document summarizes a study that used a stochastic actor-oriented model to analyze data from a randomized controlled trial in Tanzania. The trial examined how social networks influenced HIV testing rates among young men. Survey data on men's friendship networks and HIV testing behaviors were collected at three time points. The model estimated the effects of descriptive and injunctive social norms within friendship networks and across camps on changes in men's HIV testing from the second to third time points, while accounting for selection effects. The results provide insight into how social influence spreads within networks and impacts health behaviors over time.
A method to evaluate the reliability of social media data for social network ...Derek Weber
In order to study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Presented at ASONAM'20 (2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining)
Co-authors: Mehwish Nasim (Data61 / CSIRO), Lewis Mitchell (University of Adelaide), Lucia Falzon (University of Melbourne / DST Group)
Comparison of Online Social Relations in terms of Volume vs. Interaction: A C...Haewoon Kwak
This document analyzes user interactions and social relationships in Cyworld, a large online social network in Korea. It compares the declared online friendships to actual interaction data from billions of guestbook messages over 2.5 years. Key findings include:
1) The network exhibits heterogeneous relationships and assortative mixing, with a few highly connected users. Interactions between friends are highly reciprocal but disparities exist based on number of friends.
2) Microscopic analysis found that users with fewer than 200 friends have a dominant interaction partner, while those with over 1,000 friends interact more evenly. Triadic relationships are common.
3) Additional observations showed that more online friends correlated with more user activity, up to around
Understanding Collaboration in Fluid Organizations, a Proximity ApproachDawn Foster
Dawn Foster, Guido Conaldi, Riccardo De Vita
University of Greenwich
Centre for Business Network Analysis
http://www.gre.ac.uk/business/research/centres/cbna/home
Presented at the Third European Conference on Social Networks (EUSN) Mainz, Germany on 27 September 2017
This study investigates collaboration in an open source software community using proximity theory as the theoretical lens with social network analysis and modeling of activities over time to predict collaboration.
Actors in this study are part of the Linux kernel community where they collaborate on one or more sub-projects using mailing lists as the primary method of collaboration. Collaboration occurs in real-time between actors that contribute to multiple sub-projects, work for firms that pay them to contribute to the Linux kernel, and are working virtually from locations across the globe. This complex setting can be better understood by using several dimensions of proximity: organizational, cognitive, institutional, social, and geographical. Collaboration is analysed using data from source code contributions and mailing list participation.
Open source software is developed in the open where anyone can view the source code and anyone with the knowledge to do so can contribute to the project. With no central group responsible for coordination of tasks, collaboration on the development of this software is emergent. Because people from around the world work on these projects together using online tools with publicly accessible interactions between people, it is a relevant setting for using social network analysis to understand and model network relationships.
Multilevel Collaboration between Software Developers and the Impact of Proxim...Dawn Foster
This document summarizes early work on analyzing multilevel collaboration networks between software developers. The researchers aim to understand how proximity impacts collaboration within fluid organizations, using the Linux kernel open source project. Preliminary results from a relational event model found that developers were less likely to collaborate with others from the same firm or in different time zones. However, the model had limitations due to its small scale and was not yet able to fully represent collaboration across multiple levels. The researchers seek feedback on better approaches for multilevel analysis and modeling large collaborative networks.
Operationalisation of Collaboration Sunbelt 2015Dawn Foster
The operationalisation of collaboration: in search of a definition and its consequences on
analysis
Collaboration has been defined in numerous ways. Researchers interested in collaboration at the
individual or organizational level need to pay special attention to the adoption of a specific definition, as
this is likely to have major implications for the research design and outcomes. With respect to
collaboration within open source software projects, this presentation has two objectives. Firstly, this
presentation will investigate a wide variety of definitions of collaboration from the existing literature.
Secondly, the presentation will look at theoretically informed selection of a definition. Throughout the
presentation, specific emphasis will be put on the implications of adoption of several definitions of
collaboration for the application of Social Network Analysis to the study of open source software,
particularly considering data collection and analysis. Open source software is developed in the open
where anyone can view the source code and anyone with the knowledge to do so can contribute to the
project. Because people from around the world work on these projects together using online tools, it is
a relevant setting for studying collaboration. An interesting aspect of open source collaboration is that
private resources from individuals and organizations are used to develop software that is released as a
public good. Social Network Analysis can be used to understand the network relationships between the
individuals who develop this software. Given the interest in collaboration from researchers from different
backgrounds and disciplines, similar research is likely to produce considerations to stimulate further
thoughts about definitions of collaboration in several domains and research settings.
Network Relationships and Job Changes of Software Developers at Sunbelt 2016Dawn Foster
This presentation looks at job changes of software developers within an open source software community using
relational predictors of job change activity to model the actions of the actors involved. Interactions with other actors
on mailing lists and in software contributions will be used as predictors.
Open source software is developed in the open where anyone can view the source code and anyone with the knowledge
to do so can contribute to the project. Because people from around the world work on these projects together using
online tools with publicly accessible interactions between people, it is a relevant setting for studying job changes
using Social Network Analysis to understand and model the network relationships between individuals both before
and after a job change.
ATPI Doctoral Dissertation Defense of Laura A. Pasquini
Department of Learning Technologies, College of Information
University of North Texas
June 12, 2014
ICPSR - Complex Systems Models in the Social Sciences - Lecture 4 - Professor...Daniel Katz
This document provides an overview of complex systems models in social sciences, focusing on network analysis and community detection methods. It discusses key concepts like directed vs undirected networks, weighted vs unweighted edges, and overlapping vs non-overlapping communities. It also notes important considerations like network resolution, computational complexity, and how community detection results depend on the specific context and questions being examined. A variety of examples are provided, including social networks defined by friendships or voting coalitions.
Liberact conference 2013 Gnome Surfer & Moclo PlannerConsuelo Valdes
This document discusses research into using reality-based interfaces to enhance collaborative learning and discovery in fields involving large datasets, such as genomics. It presents two interactive systems developed for this purpose: G-nome Surfer, a tabletop interface for collaborative exploration of genomic data; and MoClo Planner, a collaborative tool for designing and specifying biological constructs. User studies found that both tools improved performance on genomics tasks, reduced workload, and increased enjoyment compared to traditional tools. Going forward, the research aims to explore how to better visualize and engage with large, complex datasets and facilitate collaboration across teams and locations.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
20080509 Friday Food Manchester United Business Schoolimec.archive
1. The document discusses using Small Groups As Complex Systems Theory (SGACS) as a theoretical lens to study coordination in interdisciplinary research projects.
2. SGACS provides concepts to analyze group structure, activities, outcomes, and how they are influenced by context. This framework can be used to diagnose issues, compare groups, and predict performance.
3. The document proposes applying SGACS through case studies of research groups at IBBT to gain insights on coordination challenges over time and benefits like improving project evaluations and management.
PhD proposal: Specialized heuristics for crowdsourcing website designdonellemckinley
The document discusses research on developing heuristics to support the design and evaluation of GLAM and academic crowdsourcing websites. It aims to address the lack of empirically-based guidance for these projects. The research will use Action Design Research methodology to iteratively develop a set of specialized heuristics. These heuristics will provide a tool to help meet project objectives of sufficient participation and high-quality contributions. The heuristics will also support crowdsourcing website design and evaluation practice.
Bridging the missing middle for al_tversionfinal_14_08_2014debbieholley1
Presentation to ALT-C 2014
Taking innovation from concept through to scalable delivery is complex, contested and under-theorised process. This report aims to capture the current major themes underpinning scaling, and apply these to the context of the Learning Layers project. An external review of our early ‘Design Research framework for scaling’ has highlighted that the approach is too linear and may rely too heavily on the diffusion of innovation paradigm originally proposed by Everett Rogers in the 1960s, which is less appropriate for scaling innovations in our project. Rather, we start out from design-based research principles where co-design with the users is producing both theories and practical educational interventions as outcomes of the process. This is a robust and appropriate approach suitable for addressing complex problems in educational practice for which no clear guidelines or solutions are available. We suggest that it is therefore also appropriate for multi-faceted and complex research projects such as Learning Layers.
The document summarizes a presentation about effective strategies for economic and community development. It discusses how community change issues have become more complex over time as institutions have emerged to address them. Effective strategies are characterized by network structures, asset-based frameworks, iterative planning and implementation, inclusion of short-term goals, decentralized implementation, use of metrics to learn what works, high trust among participants, and readiness for change in the community. Ineffective strategies tend to have opposing characteristics.
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
The document proposes a quantitative study to measure the correlation between individual performance of knowledge workers and their email communications. It aims to analyze email content and metadata to identify independent variables like task completion, cohesion, trust, and conflict resolution. These would be correlated with dependent variables including task value and acknowledged contributions. The study population would be knowledge workers whose email communications facilitate problem solving. It would use the Enron email corpus for data collection and apply content analysis and correlation analysis to test hypotheses about relationships between individual and team performance metrics identified in email data.
Effects of Developers’ Training on User-Developer Interactions in Information...Jennifer McCauley
The importance of user-developer interactions during the development of an information system has been a long-running theme in information systems research. This research seeks to highlight a gap in the current literature: the contribution of the developer’s formal educational background to the relationship between developers and users. Using an interpretivist epistemology, the researchers employed qualitative interviews to examine how far developers’ perception of the importance of interacting with the user was influenced by their formal education, or the lack thereof. Interviewing both formally and informally trained developers, eleven categories of interest were identified as pertinent to determining the developers’ beliefs about the importance of user interaction. Three of these categories were explored as promising for future research: academic background, work experience, and developer’s access to user knowledge. This research has implications for education of information systems developers as well as for industry interested in hiring software developers.
Similar to Collaboration between Software Developers and the Impact of Proximity (20)
The CHAOSS community develops metrics, software, and programs for observing and improving open source project health. This talk will provide an overview of the CHAOSS project along with examples of how some of these metrics are being used at VMware.
Be a Good Corporate Citizen in KubernetesDawn Foster
The document discusses how companies can be good corporate citizens when contributing to the Kubernetes community. It emphasizes balancing the needs of individuals, companies, and the community. It provides tips for effective contribution strategies, such as aligning with business goals, focusing on specific areas, growing contributors organically, mentoring others, building relationships at events, and upholding the Kubernetes community values of being inclusive and focusing on evolution over stagnation.
Overcoming Imposter Syndrome to Become a Conference Speaker!Dawn Foster
The goal of this talk is to provide some resources to help everyone feel included and welcome as a conference speaker. Open source conferences are always striving to increase the diversity of their speakers by recruiting new speakers and encouraging people from underrepresented groups to submit talks. But how do you decide what topic to cover? What can you do to help your topic stand out? How do you prevent imposter syndrome from getting in the way of your success as a speaker?
You do not need to be the world’s leading expert on a topic to give a presentation. You just need to know a few things that can help other people learn enough about the topic to get started. By bringing your authentic voice and unique perspective to the topic, people will walk away from your talk with new insights that they wouldn’t get from another speaker.
This talk will cover:
Selecting a topic and a conference for your topic.
Writing a title and abstract that will increase the chances of your talk being accepted.
The importance of your bio during the talk selection process.
Tips for writing and preparing your presentation.
The audience will walk away with practical advice about writing and submitting talk proposals along with some tips for delivering a successful presentation.
How to Be a Good Corporate Citizen in Open SourceDawn Foster
Collaboration within open source projects is becoming increasingly important for companies, but it can be difficult to strike the right balance between the needs of the company and the open source project. This can create friction and put significant pressure on employees who participate on behalf of their company when the needs of the individual, the company, and the community are not aligned. This talk will focus on ways to create this alignment between individuals, companies, and the community to help all of us be successful together.
Open Source Collaboration and Companies: Finding the Right BalanceDawn Foster
Collaboration within open source projects is becoming increasingly important for companies, but it can be difficult to strike the right balance between the needs of the company and the open source project. This can create friction and put significant pressure on employees who participate on behalf of their company when the needs of the individual, the company, and the community are not aligned. This talk will focus on ways to create this alignment between individuals, companies, and the community to help all of us be successful together.
This document discusses navigating open source project risk. It identifies several areas of risk for open source projects including ownership and governance, policies and documentation, community health, and lack of resources. It provides examples of lower risk approaches in each area such as having neutral foundations, documented processes, inclusive communities, and security policies. The document recommends making strategic decisions about risk and monitoring risks over time.
The document discusses measuring project health at VMware using custom charts generated from Augur data to cut through noise. It focuses on having thousands of repositories and hundreds of contributors/maintainers. The final thoughts emphasize interpreting metrics for improvement rather than punishment.
This document discusses navigating open source risk and provides guidance on ownership and governance, community, and resources to consider. It notes that open source projects with clear governance and neutral foundations have lower risks, as do projects with active, diverse communities that are helpful, kind and responsive. The document recommends making informed decisions about accepting and mitigating risks.
Collaborative Leadership: Governance Beyond Company AffiliationDawn Foster
Open source projects that are controlled by a single company are at a greater risk of changes that are not aligned with community interests, whereas projects that are under neutral foundations have a lower risk both for end users and software vendors. With advantages that include community building, innovation, and wider adoption, we should consider contributing more of our open source projects to neutral foundations.
This talk will cover:
* Challenges of giving up control and why it might be worth it.
* Selecting a foundation and how to determine neutrality.
* Creating a fair and neutral governance structure and processes for your project.
* Tips for contributing and maintaining your project.
The audience will get practical advice about whether they should contribute their projects to neutral foundations along with how and when to do it.
Collaborative Leadership: Governance Beyond Company AffiliationDawn Foster
The unbridled success of Kubernetes can be attributed in part to being in the CNCF. Putting Kubernetes under a neutral foundation provided a level playing field where each of us could contribute, collaborate and innovate as equals to create a widely adopted solution that we can all use. With advantages that include community building, innovation, and wider adoption, we should consider contributing more of our open source projects to neutral foundations, like the CNCF.
Collaborative Leadership: Governance Beyond Company AffiliationDawn Foster
The document discusses collaborative leadership and governance beyond company affiliation for open source projects. It addresses how governance is about the people involved and focuses on diversity and inclusion. It also covers how project ownership can take different forms like neutral foundations or company-originated, and how establishing governance processes and documentation is important to set expectations and make contributors feel welcome.
Collaborative Leadership: Governance Beyond Company AffiliationDawn Foster
The unbridled success of Kubernetes can be attributed in part to being in the CNCF. Putting Kubernetes under a neutral foundation provided a level playing field where each of us could contribute, collaborate, and innovate as equals to create a widely adopted solution that we can all use. Open source projects that are controlled by a single company are at a greater risk of changes that are not aligned with community interests, whereas projects that are under neutral foundations have a lower risk both for end users and software vendors. With advantages that include community building, innovation, and wider adoption, we should consider contributing more of our open source projects to neutral foundations, like the CNCF.
The audience will get practical advice about whether they should contribute their projects to neutral foundations along with how and when to do it.
Is this Open Source Project Healthy or Lifeless?Dawn Foster
Most of us bet large parts of our business on open source technologies, but how do we decide which projects will continue to be healthy and viable? While there are no sure bets, there are ways we can evaluate these projects to understand our risks and decide which projects are likely to be successful.
Collaboration in Linux Kernel Mailing Lists Dawn Foster
While there is quite a bit of data about the people and companies who commit Linux kernel code, there isn’t much data about how people work together on the kernel mailing lists where they decide what patches will be accepted. Using a few of the top subsystem mailing lists as examples, Dawn Foster will share her PhD research into how people collaborate on the kernel mailing lists, including network visualizations of mailing list interactions between contributors. You can expect to learn more about the people, their employers, and other data that impacts how people participate on the mailing lists. For example, do timezones influence collaboration? How about source code contributions? Dawn will also give a brief overview of her 20+ year career both before and after going back to school to get her PhD along with some information about her involvement in OpenUK.
Be a Good Corporate Citizen in KubernetesDawn Foster
As an employee, it can be difficult to strike the right balance between the needs of the company and the needs of the open source Kubernetes project. This can create friction and put significant pressure on employees who participate in Kubernetes on behalf of their company when the needs of the individual, the company, and the community are not aligned. This talk will focus on ways to create this alignment between individuals, companies, and the community required to be successful participants in Kubernetes.
Being a Good Corporate Citizen in Open SourceDawn Foster
This document discusses collaboration between individuals, companies, and communities in open source software projects. It addresses how individuals can contribute to projects, how companies can contribute resources and employees to projects, and how communities are made up of individuals. It then discusses strategies and plans for companies to contribute to open source, including aligning with business goals, identifying existing projects to contribute to, allocating resources, and measuring success. The document provides guidance on how companies can become good open source citizens, such as starting with small contributions, learning from feedback, and upstreaming patches. It emphasizes maintaining relationships with communities and attending events.
Building Community for your Company’s OSS ProjectsDawn Foster
Your company has just started an open source project, but where is the community? This talk provides practical tips and suggestions along with what not to do when building a community around your company’s open source project.
Building a community around your company’s open source project is no easy task, and there is no magic bullet or one size fits all solution. However, there are some things that you can do (or not do) to increase the chances of successfully building a community for your project.
A few of the dos and don’ts covered in this talk include:
* Planning and product management: Do use a transparent process in the open with tools that allow anyone to participate. Don’t use your internal tools and private meetings to make all of the decisions.
* Encourage participation: Do be proactive about helping community members contribute in meaningful ways. Don’t inadvertently set the expectation that employees will be the ones always answering questions and making decisions.
* Be honest: Do be honest with yourselves about where and how you prefer to have community members contribute. Don’t encourage people to contribute in areas where you are less likely to accept outside contributions.
* Managing contributions: Do have enough people trained in how to provide constructive feedback to manage the flow of incoming community contributions. Don’t assume that your existing developers have the time and skills to magically perform this difficult function.
The audience will walk away with practical advice about building communities for corporate open source projects.
Building Community for your Company’s OSS ProjectDawn Foster
Your company has just started an open source project, but where is the community? This talk provides practical tips and suggestions along with what not to do when building a community around your company’s open source project.
Building a community around your company’s open source project is no easy task, and there is no magic bullet or one size fits all solution. However, there are some things that you can do (or not do) to increase the chances of successfully building a community for your project.
A few of the dos and don’ts covered in this talk include:
Planning and product management: Do use a transparent process in the open with tools that allow anyone to participate. Don’t use your internal tools and private meetings to make all of the decisions.
Encourage participation: Do be proactive about helping community members contribute in meaningful ways. Don’t inadvertently set the expectation that employees will be the ones always answering questions and making decisions.
Be honest: Do be honest with yourselves about where and how you prefer to have community members contribute. Don’t encourage people to contribute in areas where you are less likely to accept outside contributions.
Managing contributions: Do have enough people trained in how to provide constructive feedback to manage the flow of incoming community contributions. Don’t assume that your existing developers have the time and skills to magically perform this difficult function.
The audience will walk away with practical advice about building communities for corporate open source projects.
Bad hiring managers refer to massive lists of nice-to-haves as requirements, which encourages incompetent blowhards to apply. They hire ninjas who sneak around and rock stars with huge egos. Over Dawn’s 20+ year career in technology and OSS, she has seen many terrible hiring practices at various tech companies. This Ignite talk is a slightly snarky view into what it takes to be a terrible hiring manager.
Joint talk at KubeCon San Diego 2019 with Jorge Castro.
You’re new to Kubernetes and interested in contributing, but when you start poking through the community pages, you find a bunch of SIGs and so many meetings. What’s a SIG? Where should you start? Which meetings should you attend? How can you participate?
In this talk, Jorge and Dawn from SIG Contributor Experience will live out a week within the Kubernetes community by walking the audience through what happens in this busy community. As part of the day by day tour of the community, we will cover:
* Getting started and locating meeting calendars
* Finding and participating in SIGs
* Attending meetings and what to expect
* How to get involved
* Where to get help
New contributors, users interested in contributing, engineering managers whose teams are contributing, and anyone interested in learning about new ways to get involved in the Kubernetes community will benefit from attending.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
20240609 QFM020 Irresponsible AI Reading List May 2024
Collaboration between Software Developers and the Impact of Proximity
1. Research Overview
The research uses five dimensions of
proximity theory to explore this question:
“How do participants, who are paid by
firms, collaborate within a fluid
organization?”
Despite increased participation from paid
software developers, little research has been
conducted to investigate collaboration as it
relates contributors who are employed by
firms to work within a fluid organization.
Research Setting
Linux Kernel Community Case Study1:
• Open source software
• Over 85% of contributors paid
• Neutral: competing companies
• 19M lines of code
• 11K developers
• 1200 organisations
References
1. Corbet, J., Kroah-Hartman, G. & McPherson, A., 2015. Linux Kernel Development:
How Fast is it Going, Who is Doing It, What Are They Doing and Who is Sponsoring
the Work, Available at: http://www.linuxfoundation.org/publications/linux-foundation/
who-writes-linux-2015.
2. March, J.G. & Simon, H.A., 1993. Organizations Second Ed., Malden, MA: Blackwell.
3. Dobusch, L. & Schoeneborn, D., 2015. Fluidity, Identity, and Organizationality: The
Communicative Constitution of Anonymous. Journal of Management Studies, 52(8),
pp.1005–1035.
4. Glance, N.S. & Huberman, B.A., 1994. Social dilemmas and fluid organizations,
Hillsdale, NJ: Lawrence Erlbaum.
5. Balland, P.A., 2012. Proximity and the Evolution of Collaboration Networks: Evidence
from Research and Development Projects within the Global Navigation Satellite
System (GNSS) Industry. Regional Studies, 46(6), pp.741–756.
6. Crescenzi, R., Nathan, M. & Rodríguez-Pose, A., 2016. Do inventors talk to strangers?
On proximity and collaborative knowledge creation. Research Policy, 45(1), pp.177–
194.
7. Knoben, J. & Oerlemans, L. a G., 2006. Proximity and inter-organizational
collaboration: A literature review. International Journal of Management Reviews, 8(2),
pp.71–89.
8. Cantner, U. & Graf, H., 2006. The network of innovators in Jena: An application of
social network analysis. Research Policy, 35(4), pp.463–480.
9. Boschma, R., 2005. Proximity and Innovation: A Critical Assessment. Regional
Studies, 39(1), pp. 61–74.
10. Butts, C.T., 2008. A relational event framework for social action. Sociological
Methodology, 38(1), pp.155-200.
11. Quintane, E., Pattison, P.E., Robins, G.L. and Mol, J.M., 2013. Short-and long-term
stability in organizational networks: Temporal structures of project teams. Social
Networks, 35(4), pp.528-540.
12. Opsahl, T. and Hogan, B., 2011. Modeling the evolution of continuously-observed
networks: Communication in a Facebook-like community. arXiv preprint arXiv:
1010.2141.
Method
Relational Event Framework
• Predicting events in an ordinal sequence is
product of multinomial likelihoods.10
• Ordinal model estimated using Multinomial
Conditional Logistic Regression, specifically
Cox regression estimated using MLE.11
• Using clogit in R, which is based on coxph.
• Realized event compared to 3 randomly
sampled possible events.12
• 10 day moving window.
Background
March and Simon2 define organizations as systems for coordinating activities between individuals
to facilitate cooperation with a focus on supporting decision-making processes. The notion of
organization can be expanded to include fluid organizations that emerge when people collaborate
and make decisions within a community that is recognized by its collective identity.3
Collaboration between individuals occurs within these fluid organizations; however, collaboration
within fluid organizations has been shown to reveal complex behavior with many dimensions.4
Proximity theory can been used to investigate various dimensions of collaboration5,6,7 and other
complex topics related to collaboration, such as knowledge transfer and innovation.8,9
There are several approaches to proximity theory7, and this research uses five dimensions:
cognitive, organizational, social, institutional and geographical.9
Collaboration between Software Developers
and the Impact of Proximity
Dawn M. Foster, Guido Conaldi, Riccardo De Vita
Business School, Centre for Business Network Analysis
Data
Descriptive Statistics
• Dataset: USB Mailing List (linux-usb) 2013-11-01 - 2015-11-01
• Messages (Events): 7799 in 3264 threads
• Ties: based on Ego replying to a message from Alter
• Actors: 882 (Egos: 691, Alters: 717)
Variable Operationalization
Proximity:
• Geographic: time zone similarity (temporal geo prox)
• Organizational: both work for same firm
• Social prox: # of times dyad participated in same thread
• Cognitive prox: contribute to same source code subsystems
• Institutional prox: both employed by firms
Dyadic-Level Covariates:
• Is Maintainer: one or both are in leadership (maintainer) position
• Is Committer: one or both have made code contributions
• Alter Maintainer: Alter is in a leadership (maintainer) position
Network-Level Covariates:
• Transitive closure: num of x’s ego replied to where x has replied to alter
• Cyclic closure: num of x’s alter replied to where x has replied to ego
• Shared partnership in: same x replies to both ego and alter
• Shared partnership out: ego and alter reply to messages by same x
• Repeated events: number of times ego replied to messages by alter
• Recency effect: 1/n with n as number of people alter emailed before ego
• Participation shift: 1 if last person alter replied to on mailing list was ego
xe a
xe a
e a
e a
a
1/3
1/2
1
xa e
xe a
XXXVII Sunbelt Conference
30 May 2017 – 4 June 2017
Beijing, China
Preliminary Results
• Proximity is relevant in explaining
collaboration ties within a fluid
organization.
• Preliminary results are aligned with
qualitative analysis from interviews
with software developers in this
setting.
• Further Research: Expand beyond 2
years of data from one mailing list to
see if the same results hold for other
mailing lists.
coef exp(coef) se(coef)
org proximity 5.763e-01 1.779e+00 6.280e-02 ***
social prox 3.369e+01 4.290e+14 1.047e+00 ***
cognitive prox -4.620e-01 6.301e-01 1.237e-01 ***
geo proximity 1.756e-01 1.192e+00 9.354e-02 .
inst prox (corp)2.597e-01 1.297e+00 4.535e-02 ***
is maintainer 5.128e-01 1.670e+00 1.167e-01 ***
is committer 3.335e-01 1.396e+00 5.548e-02 ***
alter maint -6.667e-01 5.134e-01 3.894e-01 .
cyclic closure 1.685e+01 2.080e+07 7.209e-01 ***
shared part in -3.263e+01 6.721e-15 1.020e+00 ***
shared part out-2.713e+01 1.653e-12 1.095e+00 ***
transitive clsr 1.060e+00 2.885e+00 5.555e-01 .
repeated events 1.684e+01 2.051e+07 5.773e-01 ***
recency effect 6.070e+00 4.326e+02 2.362e-01 ***
particip shift -3.090e+00 4.550e-02 2.386e-01 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1