This document discusses methods for analyzing personal networks derived from ego network research designs (ENRDs) and full network research designs (FNRDs). It argues that while ENRDs have advantages in terms of cost, validity of data, and ethics, FNRDs allow for additional analyses like examining non-ties, network context, and incoming ties. Both designs can be used to measure network size, composition, and homophily, though FNRDs provide more context. Overall, the document aims to compare the utility of these approaches for investigating local network effects.
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.
This document discusses social networks and social networking sites. It defines social networks as social structures made of individuals connected by relationships. It outlines the origin of social networking sites from the late 1990s and early 2000s, including sites oriented towards different purposes like professional networking, dating, shared interests, and colleges. Popular current social networking sites include Facebook, LinkedIn, Twitter, and others. The document also discusses metrics for analyzing social networks like degree centrality and betweenness centrality. It notes privacy and security concerns with social media use.
This document discusses social networks and social networking sites. It defines social networks as social structures made of individuals connected by relationships. It outlines the origin of social networking sites from the late 1990s and early 2000s and describes some of the most popular sites today, including Facebook, LinkedIn, and Twitter. It also discusses how social networks can be analyzed through metrics like degree centrality, betweenness centrality, and closeness centrality. Finally, it touches on privacy, security, and psychological issues related to social media use.
Social Network Analysis - an Introduction (minus the Maths)Katy Jordan
This document provides an overview of social network analysis concepts without advanced mathematics. It defines social network analysis as conceptualizing social relationships as links between nodes, which can be visualized and analyzed using graph theory. It discusses frequently used network metrics like degree, density, and betweenness centrality. It summarizes classic social network studies by Milgram on "six degrees of separation" and Granovetter on "the strength of weak ties." It also discusses considerations for social network analysis and tools like Gephi for visualizing networks.
Ego network analysis measures relationships between an individual (ego) and their social contacts (alters). Common measures include degree (number of alters), tie strength, multiplexity (overlap in tie functions), and alter attributes like composition, similarity to ego, and heterogeneity. Measures of relationships between alters, like density and structural holes, provide information on network constraints and opportunities. Proper data management is required to store ego, alter, and alter-alter relationships.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
This document summarizes three types of field experiments related to social networks:
1) Peer effects experiments examine whether individual j influences the behaviors or outcomes of individual i. Examples test whether encouraging individual i to vote or buy a product also influences their friend j.
2) Network formation experiments study what factors affect whether individual i forms a network tie with individual j. Examples test how anonymity, search costs, and interactions affect network tie formation.
3) Designing networks experiments evaluate which network structures maximize outcomes at the network level. Examples design peer groups and seed farmers to test how network structure impacts behavior diffusion.
The document discusses social networking sites and interpersonal relationships. It provides background on social networking usage globally and the top 10 most engaged social networking markets. The study aims to determine the influence of social networking sites on interpersonal relationships of students. Specifically, it seeks to understand students' social networking usage, interpersonal relationship levels, and the relationship between the two. The hypothesis is that there is no significant relationship between social networking and interpersonal relationships.
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.
This document discusses social networks and social networking sites. It defines social networks as social structures made of individuals connected by relationships. It outlines the origin of social networking sites from the late 1990s and early 2000s, including sites oriented towards different purposes like professional networking, dating, shared interests, and colleges. Popular current social networking sites include Facebook, LinkedIn, Twitter, and others. The document also discusses metrics for analyzing social networks like degree centrality and betweenness centrality. It notes privacy and security concerns with social media use.
This document discusses social networks and social networking sites. It defines social networks as social structures made of individuals connected by relationships. It outlines the origin of social networking sites from the late 1990s and early 2000s and describes some of the most popular sites today, including Facebook, LinkedIn, and Twitter. It also discusses how social networks can be analyzed through metrics like degree centrality, betweenness centrality, and closeness centrality. Finally, it touches on privacy, security, and psychological issues related to social media use.
Social Network Analysis - an Introduction (minus the Maths)Katy Jordan
This document provides an overview of social network analysis concepts without advanced mathematics. It defines social network analysis as conceptualizing social relationships as links between nodes, which can be visualized and analyzed using graph theory. It discusses frequently used network metrics like degree, density, and betweenness centrality. It summarizes classic social network studies by Milgram on "six degrees of separation" and Granovetter on "the strength of weak ties." It also discusses considerations for social network analysis and tools like Gephi for visualizing networks.
Ego network analysis measures relationships between an individual (ego) and their social contacts (alters). Common measures include degree (number of alters), tie strength, multiplexity (overlap in tie functions), and alter attributes like composition, similarity to ego, and heterogeneity. Measures of relationships between alters, like density and structural holes, provide information on network constraints and opportunities. Proper data management is required to store ego, alter, and alter-alter relationships.
Mathematical Models of the Spread of Diseases, Opinions, Information, and Mis...Mason Porter
This is my general-audience talk at DiscCon III (2021 WorldCon).
My talk overlapped with the Hugo Award ceremony, but the video will be posted later on the DisCon website for attendees who want to see it.
This document summarizes three types of field experiments related to social networks:
1) Peer effects experiments examine whether individual j influences the behaviors or outcomes of individual i. Examples test whether encouraging individual i to vote or buy a product also influences their friend j.
2) Network formation experiments study what factors affect whether individual i forms a network tie with individual j. Examples test how anonymity, search costs, and interactions affect network tie formation.
3) Designing networks experiments evaluate which network structures maximize outcomes at the network level. Examples design peer groups and seed farmers to test how network structure impacts behavior diffusion.
The document discusses social networking sites and interpersonal relationships. It provides background on social networking usage globally and the top 10 most engaged social networking markets. The study aims to determine the influence of social networking sites on interpersonal relationships of students. Specifically, it seeks to understand students' social networking usage, interpersonal relationship levels, and the relationship between the two. The hypothesis is that there is no significant relationship between social networking and interpersonal relationships.
This document provides an overview of special topics in social media services, including social network analysis, link mining, text mining, web mining, and opinion mining in social media. It discusses key concepts such as social network extraction, identifying prominent actors, and characteristics of collaboration networks. The document also provides examples and definitions for social network analysis metrics like degree centrality, betweenness centrality, closeness centrality, and eigenvector. Finally, it introduces relevant books and topics for further reading.
Jordan, K. (2015) Characterising the structure of academics’ personal networks on academic social networking sites and Twitter. Presentation at the Computers and Learning Research Group (CALRG) annual conference, The Open University, Milton Keynes, UK, 17th June 2015.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
This document provides an overview of community detection in networks. It begins with an introduction to the concept of communities and their usefulness in network analysis. It then discusses two main approaches to calculating communities - descriptive methods like modularity, and generative methods like stochastic block models. The document notes that community detection is an active area of research, with opportunities to extend current methods. It provides several examples of community detection applications and acknowledges contributions from other researchers in the field.
This document discusses network data collection. It begins by providing examples of how social structure matters and influences outcomes. It then discusses different ways to detect social structure through network data collection, including small group questionnaires, large surveys, observations, and digital data scraping. The document outlines key network questions that can shape data collection, such as how networks form and their consequences. It also discusses sampling and defining network boundaries. Overall, the document provides an overview of network data collection methods and considerations.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
Networks & Health
This document provides an introduction and overview of social network analysis and its relevance to health research. It discusses key concepts such as what networks are, different types of network data including one-mode and two-mode data, and different levels of analysis including ego networks, partial networks, and complete networks. The document also discusses why networks matter for health through connectionist mechanisms like diffusion and positional mechanisms like social roles. Overall, the document serves as a high-level introduction to social network concepts and their application to health research.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
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.
Visualising activity in learning networks using open data and educational ...Michael Paskevicius
Delivered October13, 2011 in Cape Town South Africa at the 2011 Southern African Association for Institutional Research forum
Abstract
As more student academic activities involve both institutional and social networks, educational analysts are needing to investigate ways in which this data can be collected and interpreted to enhance learning experiences. Data recorded as students explore personal learning environments is most often not accessible or incomplete. Here we explore some of the approaches that exist to use these social networking platforms along with information from the learning management system and academic records. Combining and analysing this data has allowed us to create a number of interesting visualizations exposing patterns which would have been impossible to glean from looking at the data alone. In an age of data abundance we reflect on using some of these new measures in relation to improving learning design, increasing academic responsiveness and enhanced student experiences.
This document discusses considerations for collecting social network data. It addresses network sampling approaches including ego network designs, complete network designs, and partial network designs. It also covers network measurement including name generators and interpreters. Additional topics include the number of name generators to use, whether to cap the number of alters elicited, specificity of relationship questions, and binary versus valued versus nested response options. The document aims to provide an overview of key issues to consider when gathering social network data.
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.
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.
The document discusses correlating scholarly networks derived from citations and social networks derived from tweets mentioning academic papers. It presents the motivation to study this correlation and describes collecting over 17,000 tweets referencing papers from ArXiv.org. Networks are constructed connecting papers mentioned by the same users, with edge weights based on time between tweets. Several network analysis metrics and case studies are discussed, finding multi-disciplinary papers are most discussed in both networks and core-periphery community structures. Areas for further work include integrating multiple social networks and modeling network dynamics over time.
This document discusses and compares features of three garage door openers: the Genie 37337R Revolution Series Close-Confirm Remote, the Chamberlain LiftMaster 387LM Universal Keyless Entry System, and the Linear MultiCode 2-Channel 12V/24V Gate Receiver. Key features highlighted include operating range, feedback on door status, compatibility with various garage door models, battery power, and weather resistance. Contact information is provided for the Garage Door Openers store.
This document provides information about a book titled "Wi-Fi Toys: 15 Cool Wireless Projects for Home, Office, and Entertainment" which was published in 2004. It includes 15 wireless projects for home, office, and entertainment use. The book was written by Mike Outmesguine and published by Wiley Publishing. It includes acknowledgments, information about the author, and a list of contributors to the book.
This document provides an overview of special topics in social media services, including social network analysis, link mining, text mining, web mining, and opinion mining in social media. It discusses key concepts such as social network extraction, identifying prominent actors, and characteristics of collaboration networks. The document also provides examples and definitions for social network analysis metrics like degree centrality, betweenness centrality, closeness centrality, and eigenvector. Finally, it introduces relevant books and topics for further reading.
Jordan, K. (2015) Characterising the structure of academics’ personal networks on academic social networking sites and Twitter. Presentation at the Computers and Learning Research Group (CALRG) annual conference, The Open University, Milton Keynes, UK, 17th June 2015.
This is my attempt at an introduction to data ethics for mathematicians. Mathematicians increasingly need to deal with these kinds of issues, but we don't have the tradition of ethics training from other disciplines.
I welcome comments on how to improve these slides. Did I miss any salient points? Do you want to offer a different perspective on any of these? Do you want to offer any counterpoints? (Please e-mail me directly with comments and suggestions.)
Eventually, I hope to develop these slides further into an article for a venue aimed at mathematical scientists, and of course I would love to have knowledgeable coauthors who can offer a different perspective from mine.
This document provides an overview of community detection in networks. It begins with an introduction to the concept of communities and their usefulness in network analysis. It then discusses two main approaches to calculating communities - descriptive methods like modularity, and generative methods like stochastic block models. The document notes that community detection is an active area of research, with opportunities to extend current methods. It provides several examples of community detection applications and acknowledges contributions from other researchers in the field.
This document discusses network data collection. It begins by providing examples of how social structure matters and influences outcomes. It then discusses different ways to detect social structure through network data collection, including small group questionnaires, large surveys, observations, and digital data scraping. The document outlines key network questions that can shape data collection, such as how networks form and their consequences. It also discusses sampling and defining network boundaries. Overall, the document provides an overview of network data collection methods and considerations.
This document provides an overview of ego network analysis. It defines ego networks as consisting of a focal individual (ego) and the people they are connected to (alters). Various measures of ego network composition, structure, and properties can be analyzed, such as size, density, and homophily. These measures provide insight into an individual's social support and influence, and can be used to study health-related questions by examining the characteristics and behaviors present in one's social network. Ego network data is relatively easy to collect and can offer information about both individuals and inferred properties of broader social networks.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the relevant population boundaries and sampling approaches. For surveys specifically, it covers informed consent, name generator questions to identify social ties, response formats, and balancing depth of network detail collected versus sample size. The key challenges are defining the theoretical population of interest, collecting a sufficiently large and representative network sample, and designing survey questions that accurately capture social ties within time and resource constraints.
Networks & Health
This document provides an introduction and overview of social network analysis and its relevance to health research. It discusses key concepts such as what networks are, different types of network data including one-mode and two-mode data, and different levels of analysis including ego networks, partial networks, and complete networks. The document also discusses why networks matter for health through connectionist mechanisms like diffusion and positional mechanisms like social roles. Overall, the document serves as a high-level introduction to social network concepts and their application to health research.
01 Introduction to Networks Methods and Measuresdnac
This document provides an introduction to social network analysis. It discusses how networks matter through two fundamental mechanisms: connections and positions. Connections refer to the flow of things through networks, viewing networks as pipes. Positions refer to relational patterns and networks capturing role behavior, viewing networks as roles. The document also covers basic network data structures including nodes, edges, directed/undirected ties, binary/valued ties, and different levels of analysis such as ego networks and complete networks. It provides examples of one-mode and two-mode network data.
In this talk we shall introduce the main ideas of TruSIS (Trust in Social Internetworking System), a Marie Curie Fellowhsip financed by European Union and hosted at VU University, Department of Computer Science, Business and Web group. The goal of TruSIS is to study the baheviour of users who affiliate to multiple social networking sites and
are active in them (e.g., users may publish personal profiles on sites like MySpace and post videos on sites like YouTube). We briefly called this scenario as SIS (Social Internetworking system).
As a first research contribution, we implemented a crawler to gather data about users and link their profiles on multiple social networking websites. To this purpose we used Google Social Graph API, a powerful API released by Google in 2008. We obtained a sample of about 1.3 millions of user accounts and 36 millions of connections between them.
Parameters from social network theory (like average clustering coefficient, network modularity and so on) were used to study the structural properties of the gathered sample and how these properties depend on user behavious.
A second contribution is about the computation of distance between two users in a SIS on the basis of their social ties. We used a popular parameter from Social Network Theory known as Katz coeffcient and
provide a computationally afficient approach to computing Katz coefficient which relies on the usage of a popular tool from linear algebra known as Sherman- Morrison formula.
Finally, we shall describe our work on extending the notion of trust from single social networks to a SIS. We describe the main research challenges tied to the definition of trust and how they relate to Semantic Web technologies.
These slides are for my talk for the Somerville College Mathematics Reunion ("Somerville Maths Reunion", 6/24/17): http://www.some.ox.ac.uk/event/somerville-maths-reunion/
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.
Visualising activity in learning networks using open data and educational ...Michael Paskevicius
Delivered October13, 2011 in Cape Town South Africa at the 2011 Southern African Association for Institutional Research forum
Abstract
As more student academic activities involve both institutional and social networks, educational analysts are needing to investigate ways in which this data can be collected and interpreted to enhance learning experiences. Data recorded as students explore personal learning environments is most often not accessible or incomplete. Here we explore some of the approaches that exist to use these social networking platforms along with information from the learning management system and academic records. Combining and analysing this data has allowed us to create a number of interesting visualizations exposing patterns which would have been impossible to glean from looking at the data alone. In an age of data abundance we reflect on using some of these new measures in relation to improving learning design, increasing academic responsiveness and enhanced student experiences.
This document discusses considerations for collecting social network data. It addresses network sampling approaches including ego network designs, complete network designs, and partial network designs. It also covers network measurement including name generators and interpreters. Additional topics include the number of name generators to use, whether to cap the number of alters elicited, specificity of relationship questions, and binary versus valued versus nested response options. The document aims to provide an overview of key issues to consider when gathering social network data.
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.
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.
The document discusses correlating scholarly networks derived from citations and social networks derived from tweets mentioning academic papers. It presents the motivation to study this correlation and describes collecting over 17,000 tweets referencing papers from ArXiv.org. Networks are constructed connecting papers mentioned by the same users, with edge weights based on time between tweets. Several network analysis metrics and case studies are discussed, finding multi-disciplinary papers are most discussed in both networks and core-periphery community structures. Areas for further work include integrating multiple social networks and modeling network dynamics over time.
This document discusses and compares features of three garage door openers: the Genie 37337R Revolution Series Close-Confirm Remote, the Chamberlain LiftMaster 387LM Universal Keyless Entry System, and the Linear MultiCode 2-Channel 12V/24V Gate Receiver. Key features highlighted include operating range, feedback on door status, compatibility with various garage door models, battery power, and weather resistance. Contact information is provided for the Garage Door Openers store.
This document provides information about a book titled "Wi-Fi Toys: 15 Cool Wireless Projects for Home, Office, and Entertainment" which was published in 2004. It includes 15 wireless projects for home, office, and entertainment use. The book was written by Mike Outmesguine and published by Wiley Publishing. It includes acknowledgments, information about the author, and a list of contributors to the book.
Int. j. of medical engineering and informatics 2012 vol.4, no.1 pp.25 35Biotechnology Consultant
Int. J. of Medical Engineering and Informatics > 2012 Vol.4, No.1 > pp.25 - 35
Title: T-cell receptor variable beta (1-24) gene repertoire in patients with Wuchereria bancrofti infections
Author: M.S. Sudhakar; K.V. Alala Sundaram; R.B. Narayanan
Address: Centre for Biotechnology, Anna University, Chennai 600 025, India. ' Department of Plastic Surgery, Royapettah General Hospital, Chennai 600 014, India. ' Centre for Biotechnology, Anna University, Chennai-600 025, India
Journal: Int. J. of Medical Engineering and Informatics, 2012 Vol.4, No.1, pp.25 - 35
Abstract: T-cell receptor V beta (TCRVβ) gene repertoire (Vβ1-Vβ24) was evaluated in the peripheral blood mononuclear cells (PBMCs) from asymptomatic and amicrofilaremic normal individuals (EN), and patients with chronic pathology (CP) harbouring Wuchereria bancrofti. TCRVβ gene expression in phytohemagglutinin (PHA) stimulated PBMC cultures from EN and CP individuals was in the order EN > CP while in Brugia malayi adult antigen (BmA) or purified protein derivative from mycobacterium tuberculosis (PPD) stimulation or the unstimulated conditions, the order was CP > EN. Thus, the PBMCs of the CP patients showed elevated levels of TCRVβ gene expression both in the unstimulated and stimulated conditions compared to EN.
Keywords: filariasis; T-cell receptor V beta; TCRV-beta; purified protein derivatives; PPD; Brugia malayi adult antigen; BmA; Wuchereria bancrofti; gene expression; phytohemagglutinin; PHA; peripheral blood cells; blood mononuclear cells; mycobacterium tuberculosis; mycobacterium TB.
DOI: 10.1504/IJMEI.2012.045301
IJPCBS 2012, 2(1), 110-116 Kavya et al. ISSN: 2249-9504
110
INTERNATIONAL JOURNAL OF PHARMACEUTICAL, CHEMICAL AND BIOLOGICAL SCIENCES
Available online at www.ijpcbs.com
ISOLATION AND SCREENING OF STREPTOMYCES SP. FROM
CORINGA MANGROVE SOILS FOR ENZYME PRODUCTION AND
ANTIMICROBIAL ACTIVITY
M. Kavya Deepthi1*, M. Solomon Sudhakar1 and M. Nagalakshmi Devamma2 1Department of Biotechnology, Rajalakshmi Engineering College, Thandalam, 2Department of Botany, Sri Venkateswara University, Tirupati, Andhra Pr Taadmesihln, aInddui,a I.n dia.
The artist creates digital graphics and 3D works. They have a portfolio of digital art including 3D creations. The document expresses gratitude for viewing the artist's works.
This document presents a method for visually exploring and comparing collections of attributed networks. The method involves reducing individual networks to class-level networks by defining actor classes based on attributes. This allows for averaging networks to show trends and variability. The approach is demonstrated on a dataset of 500 personal networks of migrants interviewed in Spain and Florida. Average networks and statistically significant differences are visualized to facilitate comparison between subgroups of migrants from different countries of origin. Refining actor classes is also discussed.
Magnum opus for Blessing White & HR Anexi HR Anexi
BlessingWhite Global Consulting has created a Leadership Magnum Opus over the last 4 decades. HR Anexi, a BlessingWhite partner, presents to you The Leadership Catalogue along with the open program ‘Training Calendar’ for the year 2013. This is your special opportunity to create your very own Magnum Opus!
All details are present in the catalogue. Kindly get in touch with the mentioned contact for further details.
HR Anexi is a strategic human capital consulting firm established in 2007 that provides HR outsourcing, organization development, talent assessment, research and employee engagement services. It has over 100 consultants and partners with other organizations to access research-based models and programs. HR Anexi believes that people are asset creators for organizations and aims to unlock their true value. It has offices in several major Indian cities and has received awards for its innovative management practices. The document provides details on HR Anexi's philosophy and capabilities across its main practice areas.
Dokumen tersebut berisi rencana pelaksanaan pembelajaran bahasa Jawa untuk kelas 2 semester 1 yang mencakup kriteria ketuntasan minimal, program tahunan, program semester, rincian minggu efektif, dan silabus."
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 discusses two main types of social network analysis: personal (egocentric) network analysis and whole (sociocentric) network analysis. It notes that personal network analysis focuses on how social context affects individuals, collecting data from respondents about their interactions with network members. Whole network analysis looks at interaction within a bounded group, collecting data from all group members. However, it notes that the distinction is not simple, as personal networks are part of the spectrum of social observations within the larger whole network of the world.
- Connectivism proposes that learning occurs through connections within networks, and is influenced by evolution over time as networks become more complex
- While connectivity has likely occurred naturally, new mathematical network analysis tools may help test whether connectivity leads to emergent behaviors
- If validated, network analysis could help optimize teaching methods by identifying influential student subgroups, at-risk students, and other insights from network dynamics
Learning with the crowd? New structures, new practices for knowledge, learning, and education
Slides for talk at Oxford Internet Institute, Bellwether lecture series: for talk, see: http://webcast.oii.ox.ac.uk.
Learning has left the classroom. It is being re-constituted across distance, discipline, workplace, and media as the social and technical interconnectivity of the Internet challenges existing structures for learning and education. The new ‘e-learning’ is more than a learning management system – it is a transformation in how, where, and with whom we learn that supports formal, informal and non-formal learning, life-long learning, just-in-time learning, and in ‘as much time as I have’ learning. But to do so, e-learning depends on the power of crowds and the support of communities engaged in the participatory practices of the Internet. We are networked in our learning, but also in our joint construction of knowledge and its legitimation, and in the social and technical practices that support knowledge co-construction, learning and education. This talk explores the emerging trends and forces that are radically reshaping learning and knowledge practices. The talk further explores the changing landscape of learning and knowledge practices with attention to motivations for contributing and valuing knowledge in crowds and communities, and the implications for future knowledge practices.
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.
This document provides an introduction to social network analysis. It discusses how network analysis allows us to understand social connections and positions. There are two key mechanisms through which networks can impact outcomes: connections, where networks matter because of what flows through them, and positions, where networks capture roles and social exchange. Network analysis provides tools to empirically study patterns of social structure by mapping relationships between actors.
This document provides an overview of social network analysis. It defines key concepts like nodes, edges, degrees, and centrality measures. It describes different types of networks including full networks, egocentric networks, affiliation networks, and multiplex networks. It also outlines common network analysis metrics that can be used to analyze networks at both the aggregate and individual level. These include measures like density, degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. The document discusses tools for social network analysis and ways of visually mapping social networks.
This document provides an overview of social network analysis (SNA). SNA is not just a methodology but a unique perspective that focuses on relationships between individuals, groups, and institutions rather than individuals alone or macro social structures. Practical applications of SNA include improving communication in organizations, identifying criminal networks, and recommending friends in social networks. The document outlines key SNA concepts like representing networks as graphs, identifying strong/weak ties, central nodes, and measures of network structure such as cohesion, density, and clustering.
This document provides an introduction to social network analysis and complex systems. It defines social networks as relationships between social entities like people and organizations. Nodes represent entities and edges represent relationships. Examples of social networks include migratory bird networks and a U.S. high school friendship network. Social network analysis is useful because human behavior is influenced by others in social contexts. Key concepts discussed include centrality measures, the small world phenomenon, and how social networks are examples of complex systems.
This document summarizes social network analysis (SNA), including its roots in sociology, key concepts, and two main approaches: sociocentric and egocentric networks. Sociocentric networks take a whole group approach and focus on relationships within bounded groups, while egocentric networks examine personal networks and connections surrounding individuals. Different data collection methods and analyses are used for each approach. The document also reviews limitations of SNA and provides references for further reading.
This document provides an overview of ego network analysis. It defines ego networks as personal networks consisting of the focal individual (ego) and the people (alters) they are connected to. The document discusses how ego network data combines aspects of traditional survey data and full network data. It describes how ego network data can be collected and analyzed. Key measures that are discussed include network size, composition, homophily, resources/risks, density, structural holes, and constraint. The document notes that ego network data allows researchers to examine individual-level network characteristics and aggregate them to make inferences about global network properties.
1. The document discusses mesoscale network structures, which are middle-scale properties between microscale (individual nodes/edges) and macroscale (overall network properties). It focuses on community structure detection but notes there are other mesoscale structures like core-periphery and roles/positions.
2. Community detection algorithms aim to find densely connected groups of nodes but may return structure even in random networks. The document advocates a cautious approach and examining multiple possible structures.
3. Other mesoscale structures discussed include bipartite structures, block models representing roles of nodes, and stochastic block models providing a statistical framework.
My slides from my 3-hour tutorial on mesoscale structures in networks from the 2016 Lake Como School on Complex Networks (http://ntmb.lakecomoschool.org/).
After my talk, Tiago Peixoto gave a talk on statistical inference of large-scale mesoscale structures in networks. His presentation, which takes a complementary perspective from mine, is available at the following website: https://speakerdeck.com/count0/statisical-inference-of-generative-network-models
This document provides an overview of social psychology, including its key topics and research methods. Social psychology is defined as the scientific study of how people think about, influence, and relate to one another. It examines how our behavior is shaped by social situations and other people. The document outlines some of social psychology's major ideas like social thinking, social influences, and obedience. It also describes common research methods in social psychology like correlational research, surveys, experiments, and the importance of ethics.
This document discusses social network analysis and its key concepts. It defines different types of networks like ego-centric and socio-centric networks. It also discusses network concepts such as density, cliques, brokers, and peripherals. The document outlines advantages of social network analysis in identifying important actors and understanding communication patterns. It also notes limitations like relying on snapshots and survey responses. Finally, it provides an example of how social network analysis was used to study business connectivity in Bristol.
The document outlines the basic steps of the scientific method used in sociological research: 1) Define the problem, 2) Review relevant literature, 3) Formulate a testable hypothesis, 4) Select a research design and collect/analyze data through methods like surveys, observation, or experiments, and 5) Develop a conclusion and ideas for further research. It discusses key aspects of each step, such as developing operational definitions, identifying independent and dependent variables, ensuring validity and reliability, and addressing ethical concerns.
Centrality in Time- Dependent NetworksMason Porter
My slides for my keynote talk at the NetSci 2018 (#NetSci2018) conference in Paris, France (June 2018). This talk will take place on Thursday 13 June in the morning.
This document discusses social network analysis and provides examples of social networks. It begins by defining what a social network is - a set of nodes connected by edges that can represent people and their relationships. It then provides examples of social networks from different domains like disease transmission, collaboration networks, and online networks. Key concepts in social network analysis like centrality, clustering, distance, and community structure are introduced. The document emphasizes that network structure can influence outcomes more than individual traits and discusses using network analysis to understand topics like information diffusion and disease spread.
heredity about humans and traits of humanshendrix18
Studies of identical twins separated at birth and raised in different families revealed that twins shared some characteristics later in life, proving that traits are influenced both by genetics and environment. While some traits are genetically inherited, others are learned from the environment in which a child is raised. One important study on this topic was conducted by Peter Neubauer and involved adopted twins who were separated at birth and unaware that they were being studied.
A beginner’s guide to social network analysis for social media and strat comm professors.
From a social network analysis fan with much to learn!
http://Netlytic.org
Overview of how to use the network visualization tool https://netlytic.org/home/?page_id=2
Tutorial for using Netlytic: https://youtu.be/F6scVtMGKFE
Additional Resources
♣ Basics of social network analysis slides
♣ Blog post “A Quick, Interactive Activity for Introducing the Concept of Digital Influencers”: http://mattkushin.com/2018/03/19/digital-influencers-easy-classroom-activity/
♣ Blog post detailing the below assignment: http://mattkushin.com/2017/04/24/teaching-basic-social-network-analysis-of-instagram-and-twitter-data-using-netlytic-org-post-4-of-4/
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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
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Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
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- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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- 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
10. General comparison ENRD and FNRD
Advantage: FNRD
• Can answer context questions
– how fragmented is the network as
a whole?
– How many links separate egos from
each other along the shortest
path?*
• Have incoming ties as well as out
– ENRD can ask ego who likes him,
but is still ego naming the alters
• Non‐ties are meaningful
– Can model tie/non‐tie for each
dyad as outcome of decision‐
making process
– in ENRD can ask ‘who do you not
like?’, but not ‘who do you have no
tie to?’
Advantage: ENRD
• Can employ standard
sampling techniques
– And so standard statistical
methods
• Cheaper & easier to deploy
– Can collect richer data – more
ties
• Fewer privacy/ethical issues
– May improve validity of data
*how quickly can something flowing through the network reach this node?
12. What can you do with ego net data?
• (if you only have) Ties to alters
– Network size for each kind of tie (e.g., number of
friends)
• (if you also have) Alter attributes
– Network composition (e.g., number of friends who
are top‐level managers)
• Testing homophily
• (if you also have) Ties among alters
– Structural holes
– All group‐level network measures (e.g., density of ties
among friends; avg distance; no. of components)
13. Number of ties
• Basically, this is network size
– Can be calculated different size for each type of tie, or all
ties combined
• Very well studied variable; has been very productive
– Health, power, satisfaction
• And there is still more to study
– Number of negative ties is understudied
• how many enemies, rivals, competitors, energy‐drains do you
have?
– Multiplex ties
• Suppose most of your friends are also co‐workers
– Most relationships A—B consist of both the friend and co‐worker tie
• What are the consequences for ego? Less freedom? More strain?
16. Network Composition
Property of network: Categorical Attributes Continuous Attributes
Summary of kind of alters ego
tends has, based on a given
attribute (e.g., wealth)
Example: Does ego have
mostly rich or mostly poor
friends? How many of each?
Example: What is the average
wealth of ego’s friends?
Measures: frequencies,
proportions
Measures: mean, median
Variability in the kinds of alter
an ego has, based on given
attribute
Example: whether ego has
equal number of rich, middle,
and poor friends, or mostly
one kind
Example: variance in wealth of
person’s friends
Measures: Blau/Herfindahl
heterogeneity; Agresti IQV
Measures: std deviation,
variance
Similarity of ego to alters with
respect to given attribute
Example: Prop. of ego’s friends
who are same wealth class as
ego
Example: Similarity between
ego’s wealth and friends’
wealth
Measures: E‐I index; PBSC;
Yules Q; Q modularity
Measures: avg euclidean
distance; identity coef.
19. Who do you discuss important
matters with?
Male Female
Male 1245 748
Female 970 1515
Age < 30 30-39 40-49 50-59 60+
< 30 567 186 183 155 56
30 - 39 191 501 171 128 106
40 - 49 88 170 246 84 70
50 - 59 84 100 121 210 108
60 + 34 127 138 212 387
White Black Hisp Other
White 3806 29 30 20
Black 40 283 4 3
Hisp 66 6 120 1
Other 21 5 3 34
Source:
Marsden, P.V. 1988. Homogeneity in confiding
relations. Social Networks 10: 57‐76.
General Social Survey 1985. Ego network study of 1500 Americans
• Rows are egos
• Columns are alters
• Cells are no. of ties
from type of ego to
type of alter
25. Homophily: preference vs opportunity
• With ENRDs we have information on ties but not non‐ties
– We can measure homophily as outcome, but not homophily as
choice
• Adequacy of homophily in ENRD depends on research
question
– If am American and 95% of my friends are American, this clearly
has certain effects on me
• even if this is only because 95% of people in my world are American
• So ENRD is ok
– But if I am trying to measure nationalistic tendencies, I need to
know whether 95% is more or less than expected if a person
were making choices without regard for nationality
• If 95% of my non‐ties are also American, we know that I am not
showing any preference for Americans – low nationalism score
26. Comparing individuals
• With ENRD, can we at least
compare egos to each other?
– Some ego’s have higher E‐I
index than others. Is this
interpretable as preference?
• In principle, yes
– if egos are drawn from the
same population, then …
– … significantly higher
homophily score indicates
greater preference for own
kind
• In practice, not clear what
“same population” means
– People live in segregated
worlds due to choices made by
others
• Example: Are male or female
students here at UAB more
homophilous with respect to
ethnic background?
• For each person, we measure
homophily using %H or E‐I
– Run t‐test/anova to compare
genders
• If all students face same ethnic
environment, then significant
difference in avg homophily is
meaningful as difference in
preference
29. Perspectives of action in SNA
Structuralist
In the social production of their existence,
men inevitably enter into definite relations,
which are independent of their will, namely
relations of production appropriate to a given
stage in the development of their material
forces of production. The totality of
these relations of production constitutes the
economic structure of society, the real
foundation, on which arises a legal and
political superstructure and to which
correspond definite forms of social conscious‐
ness. The mode of production of material life
conditions the general process of social,
political and intellectual life. It is not the
consciousness of men that determines their
existence, but their social existence that
determines their consciousness.
– Marx 1859 Preface to A Contribution to the
Critique of Political Economy
Cognitivist
“If men define situations as real, they are
real in their consequences”
– W.I. Thomas
Success
information
Actual no. of ties
confidence
Perceived no. of ties
33. Perception and ENRD
• With ENRD, all ties are perceived by ego
• Therefore, ENRD works well when …
• Predicting ego’s own behavior
• Predicting ego outcomes based on ego’s behavior
• Predicting ego outcomes AND we can assume ego is
accurate in perceiving ties
• Hard to use ENRD when the topic of interest is
understanding perceptual accuracy
– Can use hybrid designs where the alters are
interviewed about ties with ego
47. Slopes and intercepts
• Intercept is general
tendency to name others as
friends
– Gregariousness
• Slope is increase in friends
over time
• Can model via HLM
– Time is L1 unit
– Person is L2 unit
• L2 regression models slope
& intercept as function of
ego characteristics
– Optimism
– Social ability
person intercept slope
1 1.676 0.382
2 1.743 0.732
3 4.362 0.146
4 2.848 0.461
5 3.000 0.475
6 1.133 0.400
7 3.914 0.111
8 0.095 0.471
9 2.800 0.500
10 ‐1.029 0.329
11 3.276 0.307
12 1.933 0.325
13 2.638 0.379
14 2.581 0.286
15 2.524 ‐0.132
16 2.248 0.261
17 2.086 0.439
high increase
low increase
decline
49. T1Size T1 ties 3
T2Size T2 ties 3
NewTies Ties added at T2 2
LostTies Ties lost 2
KeptTies Ties present both time periods 1
AbsentTies Ties ABSENT both time periods 12
Changes for node RUSS
Changes within ego networks
T1
T2
How many ties that each node
add/drop between time points?
50. Egonet changes
T1
Size
T2
Size
New
Ties
Lost
Ties
Kept
Ties
Abse
nt
Ties
HOLLY 3 3 2 2 1 12
BRAZEY 3 3 2 2 1 12
CAROL 3 3 1 1 2 13
PAM 3 3 1 1 2 13
PAT 3 3 2 2 1 12
JENNIE 3 3 0 0 3 14
PAULINE 3 3 1 1 2 13
ANN 3 3 1 1 2 13
MICHAEL 3 3 0 0 3 14
BILL 3 3 1 1 2 13
LEE 3 3 1 1 2 13
DON 3 3 0 0 3 14
JOHN 3 3 1 1 2 13
HARRY 3 3 1 1 2 13
GERY 3 3 1 1 2 13
STEVE 3 3 0 0 3 14
BERT 3 3 1 1 2 13
RUSS 3 3 2 2 1 12
Women Men
------ ------
1 Mean 1.750 2.200
2 Std Dev 0.661 0.600
3 Sum 14.000 22.000
4 Variance 0.438 0.360
5 SSQ 28.000 52.000
6 MCSSQ 3.500 3.600
7 Euc Norm 5.292 7.211
8 Minimum 1.000 1.000
9 Maximum 3.000 3.000
10 N of Obs 8.000 10.000
Difference Sig
========== =====
-0.450 0.157
Number of ties KEPT
Significance for t‐test obtained via
randomization method
WomenMen
52. Modeling change as a function of
group membership
‐1 0 1
0 7 151 2
1 14 112 20
Chi‐Square 22.25 p = 0.001
Pearson Corr 0.10 P = 0.029
‐1 0 1 Odds Odds Ratio
0 0.044 0.944 0.013 0.013
12.540
1 0.096 0.767 0.137 0.159
Whether
alter is same
group as ego
Relationship improved (1),
worsened (‐1) or stayed same
P‐value constructed via QAP permutation test
57. Summary effects vs underlying
tendencies
• Measurements of network size, homophily,
propinquity etc can be used in two ways
– Summary of ego’s exposure to what flows
• Function of opportunities provided by environment
– Indication of ego’s strategies in tie formation
• Choices being made by the ego
• Examples
– Network size vs ability to make friends
– Observed exogamy vs preference for out marriage
ENRD FNRD
Overall effects Underlying tendencies
Consequences of homophily Reveal cognitive characteristics
58. Structuralist vs cognitivist mechanisms
• Some theoretical
mechanisms imply that
perceptions of the
network don’t matter
– Information benefits of
central position
• Others depend crucially
on perceptions
– My behavior is based on
my perceptions
• Outcomes vs behavior
• In pure ENRDs, all ties
are perceived
– Lack of true incoming
ties
– Very strong for
understanding behavior
– For understanding
outcomes, we need
additional assumption of
accuracy of perception
– People vary in
perception accuracy