Ajith Abraham, Computational social network analysis : trends, tools and research advances (Dordrecht: Springer, 2010).
CH1. An Overview of Methods for Virtual Social Networks Analysis
This document discusses using graph-based network analysis to understand social media data. It provides examples of analyzing Twitter data to identify communities of users discussing similar topics, such as groups discussing the programming language Python or reacting to a political debate. The document stresses the importance of interpreting metrics in the proper context and using graph-based analytics to better understand fragmented audiences across multiple platforms and messy social media data.
A comparative study of social network analysis toolsDavid Combe
This document compares several social network analysis tools based on their functionalities and benchmarks them using sample datasets. It finds that Pajek, Gephi, igraph, and NetworkX are mature tools that handle network representation, visualization, characterization with indicators, and community detection well. Gephi is interactive but community detection is experimental. NetworkX is attribute-friendly and handles large networks but lacks visualization. Igraph is optimized for clustering but not custom attributes. The best tool depends on the specific analysis needs.
This document 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.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 3 - Professor...Daniel Katz
This document provides a summary of Stanley Milgram's small world experiment and discussion of complex network models. It discusses how Milgram found that the average path length between individuals in society is around 6 degrees of separation. Later work by Watts and Strogatz showed that networks with a small amount of randomness can display both clustering and small world properties. Degree distributions and other network measures like clustering coefficients and connected components are discussed. Preferential attachment models that generate power law degree distributions are presented.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 2 - Professor...Daniel Katz
This document provides an overview of complex systems models in the social sciences, with a focus on network analysis. It introduces some key network concepts like nodes, edges, and the mathematical representation of relationships. It then discusses network analysis as an interdisciplinary field and provides examples of social, physical, biological, and computer science networks that have been studied. The document reviews some terminology and provides visual examples of networks. It also briefly outlines the history of network science, starting with Euler's work on the Königsberg bridge problem. Finally, it discusses some seminal network studies like Moreno's sociometry and Milgram's "six degrees of separation" small world experiment.
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.
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
This document discusses using graph-based network analysis to understand social media data. It provides examples of analyzing Twitter data to identify communities of users discussing similar topics, such as groups discussing the programming language Python or reacting to a political debate. The document stresses the importance of interpreting metrics in the proper context and using graph-based analytics to better understand fragmented audiences across multiple platforms and messy social media data.
A comparative study of social network analysis toolsDavid Combe
This document compares several social network analysis tools based on their functionalities and benchmarks them using sample datasets. It finds that Pajek, Gephi, igraph, and NetworkX are mature tools that handle network representation, visualization, characterization with indicators, and community detection well. Gephi is interactive but community detection is experimental. NetworkX is attribute-friendly and handles large networks but lacks visualization. Igraph is optimized for clustering but not custom attributes. The best tool depends on the specific analysis needs.
This document 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.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 3 - Professor...Daniel Katz
This document provides a summary of Stanley Milgram's small world experiment and discussion of complex network models. It discusses how Milgram found that the average path length between individuals in society is around 6 degrees of separation. Later work by Watts and Strogatz showed that networks with a small amount of randomness can display both clustering and small world properties. Degree distributions and other network measures like clustering coefficients and connected components are discussed. Preferential attachment models that generate power law degree distributions are presented.
ICPSR - Complex Systems Models in the Social Sciences - Lecture 2 - Professor...Daniel Katz
This document provides an overview of complex systems models in the social sciences, with a focus on network analysis. It introduces some key network concepts like nodes, edges, and the mathematical representation of relationships. It then discusses network analysis as an interdisciplinary field and provides examples of social, physical, biological, and computer science networks that have been studied. The document reviews some terminology and provides visual examples of networks. It also briefly outlines the history of network science, starting with Euler's work on the Königsberg bridge problem. Finally, it discusses some seminal network studies like Moreno's sociometry and Milgram's "six degrees of separation" small world experiment.
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.
2013 NodeXL Social Media Network AnalysisMarc Smith
Social media network analysis and visualization with NodeXL - the network overview discovery and exploration add-in for Excel. Map Twitter, Facebook, email, blogs, and the web with a point and click interface within the familiar spreadsheet.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
2010-November-8-NIA - Smart Society and Civic Culture - Marc SmithMarc Smith
This document discusses how social media and social networks are enabling new forms of civic participation and collective action. It notes that citizens are increasingly using social media to find government services, engage in discussions, and measure public opinion. The document also discusses how social network analysis can be used to analyze patterns in social media networks and identify influential users. It provides an overview of various social media platforms and the types of social networks and connections that exist within them.
This document provides an introduction to social network analysis. It discusses how social network analysis views social relationships as connections between individuals, and uses tools to systematically study these connections. The key topics covered include:
- Why social networks are important to study as they influence information and resource sharing
- The basic data elements in social network analysis, including nodes to represent individuals and edges to represent relationships between nodes
- Different levels of network data, from ego networks to complete networks
- Common ways to represent network data structurally, including graphs, matrices, and lists
- An overview of how social network analysis can help answer questions about how social relationships influence individual behaviors and the structure of social hierarchies.
2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
This document discusses key concepts in social network analysis including structuralism, social capital theory, homophily, reciprocity, and centrality. It addresses how (1) social networks can be viewed as social capital that individuals use to achieve goals; (2) both network structure and individual characteristics influence each other; and (3) real-world networks tend to exhibit properties like homophily where similar individuals connect, reciprocity in relationships, and certain influential centralized individuals.
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
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.
Harvesting Knowledge from Social Networks: Extracting Typed Relationships amo...Marco Brambilla
Knowledge bases like DBpedia, Yago or Google's Knowledge
Graph contain huge amounts of ontological knowledge harvested from
(semi-)structured, curated data sources, such as relational databases or
XML and HTML documents. Yet, the Web is full of knowledge that is
not curated and/or structured and, hence, not easily indexed, for ex-
ample social data. Most work so far in this context has been dedicated
to the extraction of entities, i.e., people, things or concepts. This poster
describes our work toward the extraction of relationships among entities.
The objective is reconstructing a typed graph of entities and relation-
ships to represent the knowledge contained in social data, without the
need for a-priori domain knowledge. The experiments with real datasets
show promising performance across a variety of domains.
The key distinguishing
feature of the work is its focus on highly unstructured social data (tweets and
Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised,
commonly assume the availability of grammatically correct language corpora.
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 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.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the boundaries of the relevant population, sampling approaches for collecting local, global or complete network data, and sources of network data including surveys, archives, and secondary data sources. The document also provides guidance on survey elements like name generators, response formats, and balancing breadth versus depth of network data collection given time constraints of surveys.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
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.
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 summarizes a workshop on social networks and network weaving. The workshop introduced concepts of networks and their benefits for social change. Participants learned about characteristics of healthy networks and the role of network weavers. The goals of the workshop were to help participants work with a network mindset and understand network theory. Participants provided input on topics for future learning community sessions focused on network mapping and applying network weaving practices to address local issues in Monterey County.
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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1. Basics of Social Networks
2. Real-world problem
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4. What graph theory problem getting from real-world problem?
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This document discusses how social media and social networks are enabling new forms of civic participation and collective action. It notes that citizens are increasingly using social media to find government services, engage in discussions, and measure public opinion. The document also discusses how social network analysis can be used to analyze patterns in social media networks and identify influential users. It provides an overview of various social media platforms and the types of social networks and connections that exist within them.
This document provides an introduction to social network analysis. It discusses how social network analysis views social relationships as connections between individuals, and uses tools to systematically study these connections. The key topics covered include:
- Why social networks are important to study as they influence information and resource sharing
- The basic data elements in social network analysis, including nodes to represent individuals and edges to represent relationships between nodes
- Different levels of network data, from ego networks to complete networks
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2009 - Connected Action - Marc Smith - Social Media Network AnalysisMarc Smith
Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
This document discusses key concepts in social network analysis including structuralism, social capital theory, homophily, reciprocity, and centrality. It addresses how (1) social networks can be viewed as social capital that individuals use to achieve goals; (2) both network structure and individual characteristics influence each other; and (3) real-world networks tend to exhibit properties like homophily where similar individuals connect, reciprocity in relationships, and certain influential centralized individuals.
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
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.
Harvesting Knowledge from Social Networks: Extracting Typed Relationships amo...Marco Brambilla
Knowledge bases like DBpedia, Yago or Google's Knowledge
Graph contain huge amounts of ontological knowledge harvested from
(semi-)structured, curated data sources, such as relational databases or
XML and HTML documents. Yet, the Web is full of knowledge that is
not curated and/or structured and, hence, not easily indexed, for ex-
ample social data. Most work so far in this context has been dedicated
to the extraction of entities, i.e., people, things or concepts. This poster
describes our work toward the extraction of relationships among entities.
The objective is reconstructing a typed graph of entities and relation-
ships to represent the knowledge contained in social data, without the
need for a-priori domain knowledge. The experiments with real datasets
show promising performance across a variety of domains.
The key distinguishing
feature of the work is its focus on highly unstructured social data (tweets and
Facebook posts) without reliable grammar structures. Traditional relation extraction approaches supervised , semi-supervised or unsupervised,
commonly assume the availability of grammatically correct language corpora.
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 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.
This document discusses considerations for collecting social network data through surveys. It addresses research design elements like defining the boundaries of the relevant population, sampling approaches for collecting local, global or complete network data, and sources of network data including surveys, archives, and secondary data sources. The document also provides guidance on survey elements like name generators, response formats, and balancing breadth versus depth of network data collection given time constraints of surveys.
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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 summarizes a workshop on social networks and network weaving. The workshop introduced concepts of networks and their benefits for social change. Participants learned about characteristics of healthy networks and the role of network weavers. The goals of the workshop were to help participants work with a network mindset and understand network theory. Participants provided input on topics for future learning community sessions focused on network mapping and applying network weaving practices to address local issues in Monterey County.
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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
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지하철 노선도 , CD 표지 , Communication : 이모티콘 Discovery : Small World problem Insight : Social Network 음 .. 예제가 적절치 못해 -_-; 앞으로 4 가지 visualization technique 를 다루게 된다 . 1) graph, 2) matrix, 3) map, 4) hybrid
“ each node can represents an actor, single or group, or a topic and each link (arc) represents the connection between couples of actors or topics.” M. C. Caschera, F. Ferri, and P. Grifoni, “[27] SIM: A dynamic multidimensional visualization method for social networks,” PsychNology Journal 6, no. 3 (2008): 291-320.
Planar graph : edge 사이에 intersection 이 발생하지 않게 그릴 수 있는 graph. 발생하지 않게 그린 graph 는 plane graph or planar embedding of the graph 라고 한다 . J Bondy, Graph theory with applications (New York: American Elsevier Pub. Co., 1976). 135 p http://en.wikipedia.org/wiki/Planar_graph Planar Graph : 평면 그래프 “ 이 planar graph 는 몇 가지 좋은 특성을 가지고 있다 . 가장 중요한 속성은 모든 planar graph 는 sparse 하다는 것이다 . Euler 의 공식 (Euler’s formula) 은 Edge E 와 Vertex V 로 이루어진 Graph 는 | E| <= 3|V| -6 임을 밝혔다 . 즉 , Edge 의 개수가 linear 함을 보여주는 것이다 . 또한 모든 planar graph 는 vertex 의 degree 가 많아야 5 이다 . 그렇기 때문에 다른 graph 에서는 polynomial time 에 해결되지 않은 알고리즘이 planar graph 에 대해서는 매우 빠르게 동작한다 . 그리고 planar graph 의 subgraph 도 항상 planar 이다 . “ http://mybox.happycampus.com/yangpa09/645528
[22] Who Shall Survive? Nervous and Mental Disease Publishing
Relationship between actors Subgroups Characteristics of the Virtual Social Networks Spring-embedding (force-directed) algorithm 노드간에 힘이 존재하여 , 보기 좋은 형태로 산개 하는 그래프 . Bernes-Hut algorithm [ 논문 ] used to efficiently compute n -body (repulsion) forces and numerical integration routines are used to smoothly update screen positions. [Vizster 논문 ] community structures Newman's community identification algorithm [ 논문 ] it provides useful topology-based groupings fast enough to support real-time interaction. [Vizster 논문 ]
Left : Kamada-Kawai Algorithm Right : Vmap-layout Algorithm
“ The matrix-based approach associates the network actors with rows and columns; the matrix cell values identify social connections between actors.” M. C. Caschera, F. Ferri, and P. Grifoni, “[27] SIM: A dynamic multidimensional visualization method for social networks,” PsychNology Journal 6, no. 3 (2008): 291-320.
Row matrix or Row vector Column matrix or Column vector Square matrix : 정방 행렬 Identity matrix : 단위 행렬 Digonal matrix : 대각 행렬 Symmetric matrix : 대칭 행렬 Skew-symmetric matrix : 반대칭 행렬 Triangular matrix : 3 각 행렬 ( 그림은 상 3 각 행렬 )
“ newsmap,” http://newsmap.jp/ . “ The map-based visualization is suitable to show and organize large volumes of data and complex social networks data structures emphasizing textural or conceptual features of the visualization by shading, colours, labelling and icons.” M. C. Caschera, F. Ferri, and P. Grifoni, “[27] SIM: A dynamic multidimensional visualization method for social networks,” PsychNology Journal 6, no. 3 (2008): 291-320.
B. A. Nardi et al., “[25] Contact Map : integrating communication and information through visualizing personal social networks,” Communications of the ACM 45, no. 4 (2002): 89.
M. C. Caschera, F. Ferri, and P. Grifoni, “[27] SIM: A dynamic multidimensional visualization method for social networks,” PsychNology Journal 6, no. 3 (2008): 291-320.