The document provides information about social network visualization and analysis. It includes contact information for librarians at UT Austin who can assist with data visualization. It discusses how to structure network data, including examples of node and edge files. Different types of networks like undirected, directed, and weighted networks are described. Centrality measures and applications of network analysis like Gephi software are also mentioned.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
This document provides an overview of social network analysis and visualization techniques. It discusses modeling and representing social networks as graphs. Key concepts in social network analysis like centrality, clustering, and path length are introduced. Visualization techniques for different types of online social networks like web communities, email groups, and digital libraries are surveyed. These include node-link diagrams, matrix representations, and hybrid approaches. Centrality measures like degree, betweenness, and closeness are also covered.
This document discusses extracting communities from web archives over time. It begins by defining key terms used, such as the web community chart and notations for time periods and communities. It then describes types of changes that can occur to communities over time, such as emerging, dissolving, growing, shrinking, splitting, and merging. It also defines metrics to measure a community's evolution, such as growth rate, stability, disappearance rate, and merge rate. The document explains how web archives are used to build web graphs and extract community structures over multiple time periods to analyze how the community structure changes dynamically over time.
This document discusses community detection in social media and online networks. It defines communities as groups of densely interconnected nodes in a graph. It outlines various algorithms for detecting communities, including graph partitioning, k-clique detection, core decomposition, divisive algorithms based on edge centrality, and modularity maximization approaches. It also discusses local community detection methods and evaluation of community detection results.
Graph theory concepts like centrality, clustering, and node-edge diagrams are used to analyze social networks. Visualization techniques include matrix representations and node-link diagrams, each with advantages. Hybrid representations combine these to leverage their strengths. MatrixExplorer allows interactive exploration of social networks using both matrix and node-link views.
Scott Gomer presented on social network analysis (SNA). He reviewed literature on SNA and its use as a tool to analyze social structures and influence. He discussed SNA's capabilities in identifying key relationships and influencers through visual sociograms. However, SNA also has limitations such as complexity with large networks. Gomer collected binary data on relationships within a network and analyzed it using sociograms to illustrate examples of social link platforms. He concluded that SNA is a qualitative tool that can provide useful insights for marketing research by studying relationships.
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.
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
This document provides an overview of social network analysis and visualization techniques. It discusses modeling and representing social networks as graphs. Key concepts in social network analysis like centrality, clustering, and path length are introduced. Visualization techniques for different types of online social networks like web communities, email groups, and digital libraries are surveyed. These include node-link diagrams, matrix representations, and hybrid approaches. Centrality measures like degree, betweenness, and closeness are also covered.
This document discusses extracting communities from web archives over time. It begins by defining key terms used, such as the web community chart and notations for time periods and communities. It then describes types of changes that can occur to communities over time, such as emerging, dissolving, growing, shrinking, splitting, and merging. It also defines metrics to measure a community's evolution, such as growth rate, stability, disappearance rate, and merge rate. The document explains how web archives are used to build web graphs and extract community structures over multiple time periods to analyze how the community structure changes dynamically over time.
This document discusses community detection in social media and online networks. It defines communities as groups of densely interconnected nodes in a graph. It outlines various algorithms for detecting communities, including graph partitioning, k-clique detection, core decomposition, divisive algorithms based on edge centrality, and modularity maximization approaches. It also discusses local community detection methods and evaluation of community detection results.
Graph theory concepts like centrality, clustering, and node-edge diagrams are used to analyze social networks. Visualization techniques include matrix representations and node-link diagrams, each with advantages. Hybrid representations combine these to leverage their strengths. MatrixExplorer allows interactive exploration of social networks using both matrix and node-link views.
Scott Gomer presented on social network analysis (SNA). He reviewed literature on SNA and its use as a tool to analyze social structures and influence. He discussed SNA's capabilities in identifying key relationships and influencers through visual sociograms. However, SNA also has limitations such as complexity with large networks. Gomer collected binary data on relationships within a network and analyzed it using sociograms to illustrate examples of social link platforms. He concluded that SNA is a qualitative tool that can provide useful insights for marketing research by studying relationships.
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.
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.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
The document discusses predicting human behavior and privacy issues in online social networks. It covers topics like understanding human behavior in social communities, user data management and inference, enabling new human experiences through reality mining and context awareness, and privacy concerns in online social networks. Architectural frameworks and methodologies are presented for managing user data, generating new knowledge, and exposing services to predict behavior and enhance experiences while maintaining user privacy.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
The document discusses the emergence of the social web and the relationship between Web 2.0 and the Semantic Web. It describes how blogs, wikis, and social networks enabled new forms of user-generated content and social interaction online in the early 2000s. The document also explains how Semantic Web technologies could enhance Web 2.0 by enabling the standardized exchange and combination of user data and services.
This document discusses ego network analysis and its advantages over sociocentric network analysis. It begins with an overview of ego networks and sociocentric networks. Ego networks have several practical advantages, including flexibility in data collection, broader inference potential, and the ability to examine overlapping social circles. However, ego networks also have disadvantages like inability to measure reciprocated ties and map broader social structure. The document then reviews common measures used in ego network analysis, including measures of network size, tie strength, composition, and homophily. It provides examples of how to operationalize these concepts.
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.
This document discusses modelling and representing social network data ontologically. It covers representing social individuals and relationships ontologically, as well as aggregating and reasoning with social network data. It discusses ontology languages like RDF, OWL, and FOAF that can be used to represent social network data and individuals semantically. It also talks about state-of-the-art approaches for representing network structure and attribute data, and the need for representations that can integrate different data sources and maintain identity.
This document provides an overview of social network analysis, including key concepts, analytic techniques, and examples of classic studies. It discusses the basic components of social networks like actors, ties, and relationships. It also describes different types of networks and measures used in social network analysis, such as degree centrality and betweenness centrality. Finally, it highlights some influential early social network analysis studies and resources for further information.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
The document discusses concepts in social network analysis including measuring networks through embedding measures and positions/roles of nodes. It covers network measures such as reciprocity, transitivity, clustering, density, and the E-I index. It also discusses positions like structural equivalence and regular equivalence and how to compute positional similarity through adjacency matrices.
UNIT 1: INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based 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.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
The document discusses principles of data visualization. It provides an overview of Tamara Munzner's framework for visualization design, which involves four levels of analysis: the domain situation, data/task abstraction, visual encoding and interaction idioms, and algorithms. The framework aims to translate real-world problems into visual representations that help users accomplish tasks. The document also outlines different types of data visualization like scientific and information visualization. Finally, it notes discoverability as a key purpose of visualization, to gain new insights from data in an interactive manner.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Visualizing Financial Stress - Talk at European Central BankKimmo Soramaki
This document summarizes a presentation given by Kimmo Soramäki on visualizing financial stress using network analysis. Some key points:
- Soramäki's 2001 analysis of the Fedwire payment network was pioneering in visualizing interconnectedness in a financial system.
- His research is highly cited in academic literature and has informed policymaking after the financial crisis.
- Soramäki has since launched a journal and software company (FNA) applying network analysis to help identify risks and emerging issues across different financial networks and datasets.
- FNA's interactive visualizations aim to help users better understand complexity and interconnectivity in areas like asset returns, payment flows, and systemic risk.
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.
Social Media Mining - Chapter 9 (Recommendation in Social Media)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
The document discusses predicting human behavior and privacy issues in online social networks. It covers topics like understanding human behavior in social communities, user data management and inference, enabling new human experiences through reality mining and context awareness, and privacy concerns in online social networks. Architectural frameworks and methodologies are presented for managing user data, generating new knowledge, and exposing services to predict behavior and enhance experiences while maintaining user privacy.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
The document discusses the emergence of the social web and the relationship between Web 2.0 and the Semantic Web. It describes how blogs, wikis, and social networks enabled new forms of user-generated content and social interaction online in the early 2000s. The document also explains how Semantic Web technologies could enhance Web 2.0 by enabling the standardized exchange and combination of user data and services.
This document discusses ego network analysis and its advantages over sociocentric network analysis. It begins with an overview of ego networks and sociocentric networks. Ego networks have several practical advantages, including flexibility in data collection, broader inference potential, and the ability to examine overlapping social circles. However, ego networks also have disadvantages like inability to measure reciprocated ties and map broader social structure. The document then reviews common measures used in ego network analysis, including measures of network size, tie strength, composition, and homophily. It provides examples of how to operationalize these concepts.
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.
This document discusses modelling and representing social network data ontologically. It covers representing social individuals and relationships ontologically, as well as aggregating and reasoning with social network data. It discusses ontology languages like RDF, OWL, and FOAF that can be used to represent social network data and individuals semantically. It also talks about state-of-the-art approaches for representing network structure and attribute data, and the need for representations that can integrate different data sources and maintain identity.
This document provides an overview of social network analysis, including key concepts, analytic techniques, and examples of classic studies. It discusses the basic components of social networks like actors, ties, and relationships. It also describes different types of networks and measures used in social network analysis, such as degree centrality and betweenness centrality. Finally, it highlights some influential early social network analysis studies and resources for further information.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
The document discusses concepts in social network analysis including measuring networks through embedding measures and positions/roles of nodes. It covers network measures such as reciprocity, transitivity, clustering, density, and the E-I index. It also discusses positions like structural equivalence and regular equivalence and how to compute positional similarity through adjacency matrices.
UNIT 1: INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based 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.
Community detection algorithms are used to identify densely connected groups of nodes in networks. Modularity optimization is commonly used, which detects communities as groups of nodes with more connections within groups than expected by chance. Parameters like resolution affect results. Multilayer networks model systems with multiple network layers over nodes. Multilayer modularity generalizes modularity to multilayer networks. Community detection in multilayer networks provides insights into structures across data types and applications.
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
The document discusses principles of data visualization. It provides an overview of Tamara Munzner's framework for visualization design, which involves four levels of analysis: the domain situation, data/task abstraction, visual encoding and interaction idioms, and algorithms. The framework aims to translate real-world problems into visual representations that help users accomplish tasks. The document also outlines different types of data visualization like scientific and information visualization. Finally, it notes discoverability as a key purpose of visualization, to gain new insights from data in an interactive manner.
UNIT III MINING COMMUNITIES
Aggregating and reasoning with social network data, Advanced Representations - Extracting
evolution of Web Community from a Series of Web Archive - Detecting Communities in Social
Networks - Evaluating Communities – Core Methods for Community Detection & Mining Applications of Community Mining Algorithms - Node Classification in Social Networks.
Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PU...Denis Parra Santander
- First version was a guest lecture about Network Visualization in the class "Data Visualization" taught by Dr. Sharon Hsiao in the QMSS program at Columbia University http://www.columbia.edu/~ih2240/dataviz/index.htm
- This updated version was delivered in our class on SNA at PUC Chile in the MPGI master program.
Visualizing Financial Stress - Talk at European Central BankKimmo Soramaki
This document summarizes a presentation given by Kimmo Soramäki on visualizing financial stress using network analysis. Some key points:
- Soramäki's 2001 analysis of the Fedwire payment network was pioneering in visualizing interconnectedness in a financial system.
- His research is highly cited in academic literature and has informed policymaking after the financial crisis.
- Soramäki has since launched a journal and software company (FNA) applying network analysis to help identify risks and emerging issues across different financial networks and datasets.
- FNA's interactive visualizations aim to help users better understand complexity and interconnectivity in areas like asset returns, payment flows, and systemic risk.
Exploratory Social Network Analysis with Pajek: Attributes & RelationsHossein Fani
The document discusses different types of properties in network analysis including relational properties that measure relationships between nodes, and non-relational properties that measure attributes of individual nodes. It describes partition properties which assign discrete categorical labels to nodes, and vector properties which assign continuous numeric values. Methods for reducing large networks through partitioning are presented, such as extracting subnetworks of nodes with the same partition value or zooming in/out based on partitions. Temporal analysis of changes in node partition values over time is also mentioned.
Applications of Network Theory in Finance and ProductionKimmo Soramaki
This document summarizes Kimmo Soramäki's presentation on applications of network theory in finance and production. The presentation discusses: 1) Using network analysis of the Fedwire payment system to identify influential banks after the 2008 financial crisis. 2) The launch of a new journal on network theory in finance with an editorial board including experts from the Bank of England and ECB. 3) Upcoming conferences in September 2015 on network theory applications in finance.
Exploratory Social Network Analysis with Pajek: DiffusionHossein Fani
This document discusses concepts related to the diffusion and spread of innovations through social networks. It covers key topics such as diffusion models like the two-step flow model, metrics for analyzing social network structures, factors that influence adoption rates like centrality and density, and concepts like critical mass and thresholds. Models of contagion are also referenced, drawing parallels to the spread of diseases.
Exploratory Social Network Analysis with Pajek: Sentiments & FriendshipHossein Fani
This document discusses theories of balance and clusterability in social networks. It introduces Fritz Heider's balance theory from the 1940s examining relationships between people, others, and topics. Balance occurs when the network can be partitioned into two clusters with positive ties within clusters and negative ties between. Later work generalized this to allow for multiple clusters. The document also discusses using Pajek software to analyze signed networks longitudinally by creating partitions and optimizing balance.
1) O documento apresenta o software Pajek para análise de redes sociais e como gerar arquivos de entrada para criação de gráficos.
2) É demonstrado como definir vértices, arestas e arcos no arquivo de entrada, além de atributos como cores e tamanhos.
3) São mostradas funcionalidades do Pajek como alterar layouts, estilos de apresentação e exportar dados.
Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.
For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/
This document discusses various techniques for visualizing networks, including different layout algorithms. It begins by defining what a network is as a data structure of entities and relationships. It then covers topics like matrix representations, arc/linear layouts, circular/chord layouts, and hierarchical edge bundling. It also discusses simple network measures like degree and betweenness centrality that can provide insight into a network's structure. The document provides many examples and references to external resources on network visualization.
Finding political network bridges on facebookNasri Messarra
Is it possible to use Facebook to identify bridges overlapping structural holes in polarized crowds on Facebook?
Experimenting on a political situation
Exploratory Social Network Analysis with Pajek: BlockmodelsHossein Fani
This document discusses blockmodeling, an exploratory social network analysis technique. It defines key terms like adjacency matrix and incident matrix. It describes different types of structural equivalence that can be used to define clusters in a network, including regular equivalence where each member in a block is connected to at least one other member of the block. An example of applying blockmodeling to analyze metal exportation networks is provided. The document also gives instructions for performing various blockmodeling operations and analyses in UCINET.
WSDM16: Temporal Formation and Evolution of Online CommunitiesHossein Fani
This document discusses research into modeling the temporal formation and evolution of online communities. It proposes:
1) Modeling each user's interests over time as a topic space, building a weighted graph between users based on topic similarity over time, and using graph clustering to identify communities of like-minded users.
2) Analyzing causality between community behaviors over time to better understand influence and predict future behaviors, noting challenges include establishing temporal precedence and improving predictions using causal information.
The research aims to better understand how interests drive similar temporal behaviors between users to form online communities.
Social Network Analysis, Semantic Web and Learning NetworksRory Sie
Session 2 of the Learning Networks Social Networks Seminar. It presents a recap of SNA terms, and introduces the Semantic Web and how it could be applied to Learning Networks.
Overview of Bibliometrics - IAP Course version 1.1Micah Altman
Whose articles cite a body of work? Is this a high-impact journal? How might others assess my scholarly impact? Citation analysis is one of the primary methods used to answer these questions.
Analyzing social media networks with NodeXL - Chapter- 09 ImagesMarc Smith
This document contains 10 figures that illustrate different types of network graphs that can be generated from message board and email list data using NodeXL software. The figures show examples of reply networks, top-level reply networks, filtering networks by subject or email address, identifying different social roles within networks, comparing networks before and after removing central members, and connecting discussion groups to member contributors. The networks provide insights into member roles and interactions within online communities.
Analyzing social media networks with NodeXL - Chapter- 05 ImagesMarc Smith
This document contains 10 figures showing social networks and how to calculate and visualize various network metrics in NodeXL. It discusses visualizing metrics like degree, betweenness centrality, and closeness centrality on two sample networks - a kite network and a network of character interactions in Les Miserables. It also shows aggregate graph metrics and frequency distributions of metrics.
Social networks are a class of information networks, where the unit of exchange (acquaintance, knowledge, attention) is in terms of information, rather than physical material. Information networks are characteristically different from material networks. While material networks are primarily about transfer of energy, information networks are driven by the need to model or represent underlying semantics. In this talk, we will first look contrast information and material networks. We will then look into different kinds of semantics that can be discerned from the way information elements have been connected.
ALIAOnline Practical Linked (Open) Data for Libraries, Archives & MuseumsJon Voss
This document discusses practical applications of Linked Open Data (LOD) for libraries, archives, and museums. It describes how LOD allows these institutions to publish structured data on the web in ways that are interoperable and can be connected to other open datasets. Examples are given of how LOD is being used by various institutions to share metadata, images, and other cultural heritage assets on the web in open, machine-readable formats. The presenter argues that LOD represents a new paradigm that these cultural organizations should embrace to make their collections more accessible and useful on the web.
Slides for talk at ConTech 2011 the International Symposium on Convergence Technology (ConTech 2011) – Smart & Humane World – on November 3rd in Seoul, South Korea.
Date: 2011 November 3 (Thurs)
Place: COEX Grand Ballroom, Seoul, Korea
Organized by Advanced Institutes of Convergence Technologies (AICT), Seoul National University (SNU)
In Cooperation with Ministry of Knowledge Economy, Ministry of Education, Science and Technology, National Research Foundation of Korea, Graduate School of Convergence Science and Technology (GSCST)
Social Network Analysis & an Introduction to ToolsPatti Anklam
This document provides an introduction to social network analysis. It discusses how networks can be mapped and analyzed using tools to understand their structure and flow of information. Key aspects of network analysis are introduced, including nodes, ties, centrality metrics, and structural patterns. A variety of tools are presented, ranging from free social media applications to specialized software, that can be used to map and analyze networks. The value of network analysis is in identifying influential individuals, improving collaboration and knowledge sharing, and intervening to change network structures and behaviors.
LSS'11: Charting Collections Of Connections In Social MediaLocal Social Summit
Keynote Title: Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
Abstract: Networks are a data structure common found across all social media services that allow populations to author collections of connections. The Social Media Research Foundation‘s NodeXL project makes analysis of social media networks accessible to most users of the Excel spreadsheet application. With NodeXL, Networks become as easy to create as pie charts. Applying the tool to a range of social media networks has already revealed the variations present in online social spaces. A review of the tool and images of Twitter, flickr, YouTube, and email networks will be presented.
This document provides an overview of social network analysis and the Sylva software. It begins with key concepts in social network analysis including social structure, social networks, nodes, linkages, and additional terminology. It then discusses what makes social network analysis unique and provides examples of ego-centered and community-centered network analysis. Finally, it describes the features and capabilities of the Sylva software for collecting, storing, visualizing, and analyzing social network data.
Tutorial on Relationship Mining In Online Social Networkspjing2
This document provides a tutorial on relationship mining in online social networks. It begins with introductions to basic concepts like defining the relationship mining task and relationship concepts from sociology. It then discusses how text mining can help with relationship mining by extracting features from text data. It outlines several sub-fields for relationship mining, including data acquisition/storage, different relationship mining approaches, and associating user attributes with relationships. The document concludes by discussing specific relationship mining systems.
The document discusses social networks and computer science. Some key points:
1. Social networks can be represented as graphs with people as vertices and relationships as edges. Quantitative analysis looks at graph properties and constructs models to explain them.
2. Computer science can help study large-scale social networks through processing power, storage, and algorithms. Areas of study include network structure, how networks change over time, and designing tools to monitor/predict changes.
3. Structural properties like degree distribution, connected components, and small world phenomena are observed in social networks. The dynamics of link formation, homophily, and positive/negative links are also examined.
This document summarizes a presentation on using bibliographic couplings to analyze the structure of a large public university. The presentation analyzed co-citation networks between university scholars and the papers they cited to identify overlapping intellectual communities across disciplines. It identified key individuals who bridge communities and act as knowledge conduits. The analysis found that the network of engaged scholars bears little resemblance to the existing academic units, suggesting opportunities to restructure the university to better support interdisciplinary work.
20120301 strata-marc smith-mapping social media networks with no coding using...Marc Smith
NodeXL is a free and open social network analysis add-in for Excel that makes analyzing social media networks as easy as making a pie chart. It allows users to import social media network data from various sources, visualize the resulting networks, and perform automated network analysis and metrics calculations with just a few clicks. The tool aims to open up social network analysis to more users by simplifying the traditionally complex processes of data collection, network mapping, and metrics calculation.
Slides | Research data literacy and the libraryColleen DeLory
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Sarah Wright, Christian Lauersen and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Slides | Research data literacy and the libraryLibrary_Connect
Slides from the Dec. 8, 2016 Library Connect webinar "Research data literacy and the library" with Christian Lauersen, Sarah J. Wright and Anita de Waard. See the full webinar at: http://libraryconnect.elsevier.com/library-connect-webinars?commid=226043
Finding Knowledge: Assessing Knowledge in the Age of SearchSimon Knight
This document discusses how epistemology relates to allowing internet access during exams. It notes that Denmark allows internet access during exams to test problem-solving and analysis skills. While no communication sites are allowed, the policy is based on epistemological claims about the nature of knowledge. It also discusses the risks of injustice and content holes when relying on internet searches due to filter bubbles, bias, and a lack of opposing views. Throughout, it emphasizes that both search tools and students need to consider multiple perspectives and the assumptions underlying information to gain an accurate understanding.
Linked Open Data in Libraries, Archives & MuseumsJon Voss
This document provides an overview of Linked Open Data for libraries, archives, and museums. It discusses the growing movement of LODLAM and how it allows these cultural institutions to represent their data as graphs using triples that describe entities in a machine-readable format. Key concepts covered include the use of URIs, RDF, vocabularies, and different legal tools for publishing open data.
About the Webinar
The library and cultural institution communities have generally accepted the vision of moving to a Linked Data environment that will align and integrate their resources with those of the greater Semantic Web. But moving from vision to implementation is not easy or well-understood. A number of institutions have begun the needed infrastructure and tools development with pilot projects to provide structured data in support of discovery and navigation services for their collections and resources.
Join NISO for this webinar where speakers will highlight actual Linked Data projects within their institutions—from envisioning the model to implementation and lessons learned—and present their thoughts on how linked data benefits research, scholarly communications, and publishing.
Speakers:
Jon Voss - Strategic Partnerships Director, We Are What We Do
LODLAM + Historypin: A Collaborative Global Community
Matt Miller - Front End Developer, NYPL Labs at the New York Public Library
The Linked Jazz Project: Revealing the Relationships of the Jazz Community
Cory Lampert - Head, Digital Collections , UNLV University Libraries
Silvia Southwick - Digital Collections Metadata Librarian, UNLV University Libraries
Linked Data Demystified: The UNLV Linked Data Project
This document discusses using user experience (UX) design and data visualization to better understand complex data. It introduces Paula de Matos and Jason Dykes who are experts in UX and visualization. They provide an example scenario about designing a library visualization to help a local authority research officer determine which libraries are most successful. Participants are tasked with sketching a visualization to help address this scenario. The document also discusses challenges of UX design for complex data environments and provides an example of applying a UX process to develop an enzyme portal for bioinformatics data.
The document discusses linked data and its use in libraries. It describes how linked data can make implicit information explicit by using vocabularies and ontologies. Linked data takes advantage of web standards to better describe resources and make them easier to find. It addresses the need for a "library shaped hole" on the internet and the benefits of moving library data out of silos and enabling reuse through a MARC replacement like BIBFRAME. Challenges in transforming data and transitioning to new terminology are also discussed.
Intro to Linked Open Data in Libraries Archives & Museums.Jon Voss
This document discusses a presentation on Linked Open Data in libraries, archives, and museums. The presentation introduces Linked Open Data and how it is being used in cultural heritage institutions. It discusses representing data as graphs using triples and RDF, important vocabularies and ontologies, and following Tim Berners-Lee's principles of Linked Data. The presentation also covers legal and licensing considerations for publishing open cultural data on the web.
Delivered by Peter Burnhill, Director of EDINA, at the PRELIDA Consolidation and Dissemination workshop on 17/18 October 2014 (http://prelida.eu/consolidation-workshop).
Summary: The web changes over time, and significant reference rot inevitably occurs. Web archiving delivers only a 50% chance of success. So in addition to the original URI, the link should be augmented with temporal context to increase robustness.
This document discusses authors' rights when publishing their work. It explains key concepts like copyright, different publishing agreement models (work made for hire, copyright transfer, exclusive license, non-exclusive license), and actions authors can take to retain more of their copyrights like using addendums. The goal is to help authors understand their rights and negotiate with publishers to allow broader sharing and reuse of their published work.
This document discusses options for publishing in digital humanities, including traditional journals, digital humanities journals, and nontraditional formats. It addresses factors to consider such as goals, audience, discoverability, collaboration, and intellectual property. Guidelines are provided for evaluating traditional publication options, including reviewing publisher reputation, editorial boards, and copyright policies. Resources like curated journal lists and copyright guides are also referenced. Attendees are encouraged to complete a workshop survey.
This document discusses data literacy for humanities research. It defines data and explains that data comes in many forms including audio, text, and geospatial information. Data literacy involves understanding data quality, structure, and context. The document outlines different types of humanities data and discusses how data can be big or small. It emphasizes understanding the context, source, and potential biases of data. The document provides examples of descriptive analysis and data wrangling challenges. Throughout, it stresses investigating data provenance and recognizing when data may be uncertain or misleading.
The document outlines the goals and steps for creating a WordPress site, including customizing privacy settings, adding pages and blog posts, choosing a theme, and connecting social media. The goals are to create a WordPress site, modify privacy settings, add and customize content, and use the site for blogging. It provides instructions on setting up an account, navigating the dashboard, creating pages and blog posts, selecting a theme, and setting the front page. It also discusses deleting sites, inviting contributors, and integrating social media through links, embeds, or widgets.
Outline
Digital Project Planning
What is the goal of your Digital Scholarship project?
We will discuss Digital Humanities projects as Digital Scholarship Project
Learn what the components or layers of a Digital Humanities project are.
How do you find data to use to answer research questions?
Understand descriptive metadata and the rationale for its use
Digital Pedagogy
If you are involving students how does that affect your planning plan?
How do you incorporate Digital Pedagogy into a Digital Project?
This document provides instructions for customizing a WordPress site, including creating pages and blogs, modifying themes, embedding code and adding widgets. It includes steps for creating a teaching portfolio by modifying pages and themes. It also describes how to add social media links, embed content using HTML, and add a Twitter widget. The document concludes by explaining how to delete or hide an entire WordPress site.
Are you interested in finding and using digital tools to enhance your research? In this workshop, Rafia Mirza from the UT Arlington Central Library will introduce you to the many different tools that are available to help you gather, process, and present your research.
Goals of today’s workshop
Part 1: Create a Wordpress Site
Part 2: Customize your site.
Example: Modify your site to create a Teaching Portfolio
Part 3: Customize your blog
Part 3: Embedding code in your blog
Part 5: Delete a Wordpress Site
Your Digital Identity: Social Media & Online Presencelibrarianrafia
This document provides information on managing your digital identity and online presence as a scholar. It discusses social media platforms like Twitter and how they can be used for professional purposes like networking at conferences. It also covers representing yourself online through platforms like Academia.edu, ResearchGate, and institutional repositories. Maintaining privacy and copyright over your work are also addressed. The goal is to help scholars strategically build and control their digital identity.
Faculty collaboration, memorandum of understanding, and open access
Presentation by: Jeff Downing, Digital Projects Librarian (downing@uta.edu) and Rafia Mirza, Digital Humanities Librarian (rafia@uta.edu)
Presented at CTLC Scholarly Communication Affinity Meeting, August 5th, 2016
Creating a Process for Successful Collaboration
CTLC: Cross Timbers Library Collaborative (CTLC) Conference July 22, 2016
Conference site https://www.ct-lc.org/Events/2016-Conference
Memorandum of Understanding Workshop: Creating a Process for Successful Digit...librarianrafia
When working on digital projects, it is necessary to utilize experience in various departments within and outside of the library. A planning document called a Memorandum of Understanding (MOU) serves as an agreement between all stakeholders, which will likely include multiple library departments. In order to set expectations, an MOU can assist in the following ways: (1) Evaluating current and potential infrastructure; (2) Determining whether funding is needed or available, (3) Establishing clearly demarcated responsibilities and outcomes for each individual participant, (4) Accounting for and settling potential disagreements, and (5) Serving as a project management plan.
Introduction to databases and metadata
Outline
What are databases?
What are the elements of databases?
What is metadata?
Why are they important for digital projects?
What is the internet?
What is a search engine?
What do search engines not search?
What if you are getting too many results?
What are filters?
What if the results are not relevant?
Digital Humanities for Historians: An introductionlibrarianrafia
What is Digital Humanities (DH)?
What is Digital History?
What is Cliometrics?
What is the Spatial Turn?
What goes into creating a Digital Humanities project?
What are some of the resources available for DH?
What are some of the debates in DH?
Attribution-NonCommercial 2.5 Generic (CC BY-NC 2.5) for all original content in presentation.
The UTA Libraries offer digital humanities services including consultations and workshops through their Digital Humanities Librarian for both undergraduates and graduate students in collaboration with various programs. They provide subject guides and assistance with platforms like Omeka. Copyright consultations are available to ensure legal compliance when building and using digital collections. The libraries are expanding their digital collections through projects digitizing special collections materials on topics related to the borderlands such as the U.S.-Mexico War and Tejano Voices.
Using Omeka as a Gateway to Digital Projectslibrarianrafia
Digital Frontiers 2015 https://digital-frontiers.org/ Presentation on Omeka 9/18/2015
Presenters: Jeff Downing, Lynn Johnson, and Derek Reece (Digital Projects Librarians) and Rafia Mirza (Digital Humanities Librarian)
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
2. Contact Information
Rafia Mirza | Digital Humanities Librarian
@LibrarianRafia | rafia@uta.edu
Peace Ossom Williamson | Director for Research Data Services
@123POW | peace@uta.edu
3. How to viz
1. Determine what to say
2. Find/collect the data you need
3. Wrangle!
4. Clean!
(Repeat 3 & 4 many more times)
5. Come to a final product/conclusion
5. Data Visualization
The graphical display of
abstract information for two
purposes:
• sense-making
• communication
https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-
visualization-for-human-perception
Others
52%
Heather 1%
Lydia 6%
Peace
13%
Kaeli
28%
Answering Nursing Questions
Most nursing questions are not reaching the
nursing team, as we are answering fewer than
50% recorded.
0
20
40
60
80
100
120
12
am
1 2 3 4 5 6 7 8 9 10 11 12
pm
1 2 3 4 5 6 7 8 9 10 11
pm
Hourly Question Frequency
Chat Questions Total Questions
6. Data Visualization
The graphical display of
abstract information for two
purposes:
• sense-making
• communication
5
11
36
58
61
102
202
409
0 100 200 300 400 500
In-Person
Ask a Librarian
Blank
Phone
Appointment
Drop-In
Email
Chat
Number of questions per semester
How Nursing Questions are Received
Chat is the primary method, followed by emails.
Together, they make up 70% of incoming nursing
questions.
https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-
visualization-for-human-perception
7. Data Visualization
The graphical display of
abstract information for two
purposes:
• sense-making
• communication
https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/data-
visualization-for-human-perception
Potential Actions:
• Put nursing librarians on chat
• Inform staff on chat how to answer
common questions
• Make contact methods clear to nursing
students
• Be available during hours questions are
most often asked
17. Social network analysis (SNA)
• “Social network analysis (SNA) is the process of investigating social
structures through the use of network and graph theories. It
characterizes networked structures in terms of nodes (individual
actors, people, or things within the network) and the ties, edges, or
links (relationships or interactions) that connect them.”
• via Wikipedia
• History of Network Analysis (link to ppt)
18. Social network analysis in History
• Networks in Historical Research
• “The use of formal network methods for historical research is much more
recent, with only a few exceptions dating back beyond thirty years.”
• Historical Network Research: Network analysis in the historical
disciplines
• “Among historians, the term network has been used in a metaphorical sense
alone for a long time. It was only recently that this has changed.”
19. Social network analysis in History
• Visualizing Historical Networks Website
• “Kindred Britain is a network of nearly 30,000 individuals — many of
them iconic figures in British culture — connected through family
relationships of blood, marriage, or affiliation. It is a vision of the
nation’s history as a giant family affair.”
26. How do you structure your data
to do network viz?
27. Setting Up the File
Node 1 Node 2
Peace Rafia
Peace David
Rafia Peace
Peace
Rafia David
28. Setting Up the File
Source Destination Weight
B A 1
B E 1
C A 1
C E 1
C D 1
A
B C D
E
29. Setting Up the File
A B C D E
A 0 0 0 0 0
B 1 0 0 0 1
C 1 0 0 1 1
D 0 0 0 0 0
E 0 0 0 0 0
A
B C D
E
30. Setting Up the File
Source Destinations
B A E
C A D E
A
B C D
E
31. Setting Up the File
NodeID Attr1 Attr2
A yellow 1
B green 3
C orange 5
D yellow 3
E blue 1
A
B C D
E
32. Attractiveness
Ensure the following:
• Related nodes are close
• Groups of related nodes are
clustered together
• Sufficient empty space between
nodes, minimal overlapping
http://www.markowetzlab.org/pics/dyNet_fig2.jpg
37. Quality function to be minimized:
• xi – location of node i
• aij – weight of edge between notes i and j
• α and β – attraction and repulsion parameters
Statistics
44. Applications
Creating
• Gephi
• Cytoscape
• NodeXL
• Sci2
• igraph
FILE TYPE Edge List/
Matrix
XML Edge
Weight
Attributes Viz
Attributes
Hierarchal
Graphs
CSV
DL Ucinet
DOT Graphviz
GDF
GEXF
GML
GraphML
NET Pajek
45. Gephi: The Open Graph Viz Platform
Gephi is the leading visualization and exploration software for all kinds of graphs and networks. Gephi is open-source and
free.
Learning Outcomes
Understand the basic use of visualizations in network analysis
What is
Data Viz?
Network Viz?
SNA
What types of Networks are there?
How do you structure your data to do a viz?
Recognize visualization file setup and structure
What applications can you use?
Distinguish systems or programs that create network visualizations
What applications can you use?
What are some tools/programs you can use?
Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication. Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others.
Image:
1st one: Area chart: showing how many questions received each hour of the day
2nd one: pie chart: what % of questions coming form nursing dept., who is answering
Sense making, data that otherwise you could not see patterns in if you had not visualized
Communication: now communicating for purpose, marketing, argument, etc.
Bar charts: shows most ? Coming in chat or email, could use to make argument need to increase staff on those
Can also do both. What is your end goal?
What is your thesis? If no purpose then not very useful.
The information is abstract in that it describes things that are not physical. Statistical information is abstract. Whether it concerns sales, incidences of disease, athletic performance, or anything else, even though it doesn't pertain to the physical world, we can still display it visually, but to do this we must find a way to give form to that which has none. This translation of the abstract into physical attributes of vision (length, position, size, shape, and color, to name a few) can only succeed if we understand a bit about visual perception and cognition. In other words, to visualize data effectively, we must follow design principles that are derived from an understanding of human perception.
So, what is a network? A network is any collection of objects in which some pairs of these objects are connected by links/edges (Easley & Kleinberg, 2011). Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself.
In this visualization, the focus is on specifying relationships among a collection of items.
A network consists of nodes, edges, and possibly spatial information. Statistics are associated with the nodes and the edges. Nodes (or objects) are connected by links (or edges). The edge statistics may be directed, or pointed, or undirected.
The network may have a natural spatial layout as does a geographical trade-flow network, or may be abstract as in a personal communications network. Network data may categorical, such as the type of node or edge, or quantitative such as a edge’s capacity. The data may be static, such as a network’s capacity, or time varying, such as the network flow in several time periods.
Modern network visualization goes back to Jacob L. Moreno’s “Sociograms” of the 1930s. He standardized the usage of circles and lines to represent agents and their relations (1932). He introduced shapes to mark different groups of nodes and used arrow heads to show directionality of connections (1934). Decades before computer programs were available, Moreno positioned the nodes to reveal social structure (1934). In subsequent years, important nodes were arranged centrally in radial layouts (Lundberg and Steele, 1939) or were drawn with larger circles (Loomis, 1946). Contextual information played an ever greater role in network visualizations—Roethlisberger and Dickson (1939) positioned nodes based on their physical location; Davis, Gardner, and Gardner (1941) used socio-economic variables to position the nodes; and Sampson (1968) mapped positive and negative sentiment towards agents to node positions. However, some remarkable network visualizations had already been published as early as the late nineteenth century. These visualizations include Alexander Macfarlane’s visual representation of British marriage prohibitions (1883) and John Hobson’s approach of visualizing two-mode data by showing the overlap of directors among South African companies (1894). For this poster presentation, we have re-created the above mentioned visualizations as well as other historic network figures. All of them represent milestones in the development of network visualizations that guide contemporary network visualizations through today.
Bernfeld – circle of girl friends. Four figures show different relations. Line thickness represents intensity.
Moreno – class structure, 5th grade. Girls (circles) and boys (triangles). Links show two best friends. Top line represents group border
Roethlisberger – observed friendship ties and cliques in a factory. Position reflects the location of their workspace.
Davis – a group of women in Old City, 1936. Women participating at social events. Rows and columns were rearranged to show groups.
Many kinds of network viz, but what is you lens/methadology/purpose
In the 1960s, Eugene Garfield created the “historiograph”, a technique to visualize the history of scientific fields using a network of citations or historical narratives laid out temporally from top to bottom.1 Garfield developed a method of creating historiographs algorithmically, and his contemporaries hoped the diagram would eventually be used frequently by historians. The idea was that historians could use these visuals to quickly get a grasp of the history of a discipline’s research trajectories, either for research purposes or as a quick summary in a publication.
Undirected: no direction, mutual, so no arrows
Facebook friends
Who lived in same era
Twitter is directed (not mutual), has arrows,
Twitter you can follow , but they don’t follow back,
Hierarchical relations, , leaders such as kings, queens, generals, etc.
Communication (letters, epistles, etc)
Weighted when you weigh size of edge by intensity.
So example, Facebook likes, maybe you like everything someone posts, (thick line) but they only like a 1/3 of what you post (thinner line)
Adjacency: Facebook, twitter, how one node connects to another
Affiliation: two/multiple nodes , how one type of node is connected to another type of node (could show people and party affiliation)
You are going to need to figure out what tool you are using, so you are going to set up file in a way that the tool can read. Here are some examples of what that will look like
One type of file set up
Example:
1st column, originator node, to destination node
Peace is listed in two rows, because point to different names
Example: who writes each other letters ?
One type of file set up
Similar file set up to previous, but weight added
Has different headers
Headers dictated by what you want to show and how program you are using want file structured
One type of file set up
Column names will be reflected in viz, do you want to name column a A or Alison?
Example:
All potential recipient listed across top
All potential originators listed across left
For this file type all destination need to be on same line
For this file, type, program says if you want nodes to have color, and size you have to add that as an attribute
Best practices is Attractiveness may depend on the type of visualization (static versus interactive) and the type of network (size and density)
How people parse information
Attractiveness may depend on the type of visualization (static versus interactive) and the type of network (size and density)
Degree – how many other nodes are directly reached by this node?
Betweenness – how likely is this node to be the direct route between two other nodes in the network?
Closeness – how fast can this node reach every other node in the network?
Eigenvector – how well is this node connected to other well-connected nodes?
Network metrics
This is a viz that shows degree and betweeness of Forrest and Abbie
Degree – how many other nodes are directly reached by this node?
Betweenness – how likely is this node to be the direct route between two other nodes in the network?
Forrest has high degree and betweeness, so he is in middle of network (and example of how you might manually adjust viz to crate understanding, manually made his dot bigger)
Abbi has low.
Star Wars social networks: The Force Awakens
http://evelinag.com/blog/2016/01-25-social-network-force-awakens/#.WIGLFMtMHqA
“ The nodes in the network represent the individual characters, and they are connected by a link if they both speak within the same scene. The width of each link represents the frequency of co-occurrence of the two corresponding characters, and the size of each node represents the number of scenes where the character speaks.”
Closeness – how fast can this node reach every other node in the network? Shows how many times characters have talked to each other (how fast you can reach 1 to the other)
Eigenvector – how well is this node connected to other well-connected nodes? Rd-d2 has high eigenvector
We will be using Google fusion tables to create a basic summary table and a chart.
Network viz don’t just happens, stats are involved that determine how layout is going to be
This is repulsion and attraction settings
Alpha attraction
Beta is repulsion
As attraction increases, nodes get closer together
As repulsion increases, nodes move further apart
Decrease repulsion
Very sparse networks may benefit from negative repulsion
-low repulsion leads to more uniform and less clustered layouts, which may be attractive for static visualizations
Often, this is the best layout for interactive visualizations
Reduced repulsion here, if you wanted networks/clusters to be closer together
Default values which usually work reasonably well for both static and interactive visualizations.
Coauthor network
This is a setting, what do you want your attraction and repulsion settings to be
This is Default
My LinkedIn network: Left – college, Right – librarians, Lower – UTA nursing faculty, dots – random people (running partner, best friend since HS, personal trainer)