Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding

Transcription

1 Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding A project from the Social Media Research Founda8on: h:p://

2 About Me Introduc8ons Marc A. Smith Chief Social Scien8st Connected Ac8on Consul8ng Group h:p:// h:p:// h:p:// h:p://delicious.com/marc_smith/paper h:p:// h:p:// h:p:// h:p:// h:p://

3 h:p://

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6 Social Network Theory h:p://en.wikipedia.org/wiki/social_network Central tenet Social structure emerges from the aggregate of rela8onships (8es) among members of a popula8on Phenomena of interest Emergence of cliques and clusters from pa:erns of rela8onships Centrality (core), periphery (isolates), betweenness Methods Surveys, interviews, observa8ons, log file analysis, computa8onal analysis of matrices Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communica8on, Simon Fraser University. pp (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)

7 SNA 101 B D F H A E I C G Node actor on which rela8onships act; 1- mode versus 2- mode networks Edge Rela8onship connec8ng nodes; can be direc8onal Cohesive Sub- Group Well- connected group; clique; cluster Key Metrics Centrality (group or individual measure) Number of direct connec8ons that individuals have with others in the group (usually look at incoming connec8ons only) Measure at the individual node or group level Cohesion (group measure) Ease with which a network can connect Aggregate measure of shortest path between each node pair at network level reflects average distance Density (group measure) Robustness of the network Number of connec8ons that exist in the group out of 100% possible Betweenness (individual measure) # shortest paths between each node pair that a node is on Measure at the individual node level Node roles Peripheral below average centrality C Central connector above average centrality Broker above average betweenness A B D E E D

8 (and more) is from people to people 8

9 PaDerns are lee behind 9

10 There are many kinds of 8es. h:p://

11 World Wide Web Each contains one or more social networks

12 Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts and Answer People Discussion people, Topic se:ers Discussion starters, Topic se:ers

13 Tag Ecologies I Adamic et al. WWW 2008

14 Networks reveal pa:erns HUB- AND- SPOKE OF DECEIT: When Enron employees communicated about legi8mate projects, e- mails were reciprocal and informa8on was shared widely (right), but communica8ons about an illicit project (les) reveal a sparse network with a central, informed clique and isolated external players. Brandy Aven, CMU h:p:// Informa8on_flow_can_reveal_dirty_deeds

15 Goal: Make SNA easier Exis8ng Social Network Tools are challenging for many novice users Tools like Excel are widely used Leveraging a spreadsheet as a host for SNA lowers barriers to network data analysis and display

16 Who we are People Disciplines InsGtuGons University Faculty Computer Science University of Maryland Students HCI, CSCW Oxford Internet Ins8tute Industry Machine Learning Stanford University Independent Informa8on Visualiza8on Microsos Research Researchers UI/UX Illinois Ins8tute of Technology Developers Social Science/Sociology Connected Ac8on Network Analysis Collec8ve Ac8on Cornell Morningside Analy8cs

17 What we are trying to do: Open Tools, Open Data, Open Scholarship Build the Firefox of GraphML open tools for collec8ng and visualizing social media data Connect users to network analysis make network charts as easy as making a pie chart Connect researchers to social media data sources Archive: Be the Allen Very Large Telescope Array for Social Media data coordinate and aggregate the results of many user s data collec8on and analysis Create open access research papers & findings Make collec3ons of connec3ons easy for users to manage

18 What we have done: Open Tools NodeXL Data providers ( spigots ) ThreadMill Message Board Exchange Enterprise Voson Hyperlink SharePoint Facebook Twi:er YouTube Flickr

19 What we have done: Open Data NodeXLGraphGallery.org User generated collec8on of network graphs, datasets and annota8ons Collec8ve repository for the research community Published collec8ons of data from a range of social media data sources to help students and researchers connect with data of interest and relevance

20 What we have done: Open Scholarship

21 What we have done: Open Scholarship

22 Social Media Research Founda8on h3p://smrfounda:on.org

23 NodeXL Network Overview Discovery and ExploraGon add- in for Excel 2007/2010 Heather has high betweenness A minimal network can illustrate the ways different loca8ons have different values for centrality and degree

24 Now Available

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26 h:p://

27 h:p:// hogans- facebook- social- network- data- provider- and- visualiza8on- toolkit/

28 Network of connec8ons among the people who tweeted the term PAWCON on 19 October 2011

29 NodeXL data import sources

30 Example NodeXL data importer for Twi:er

31 NodeXL imports edges from social media data sources

32 NodeXL Automa8on makes analysis simple and fast

33 NodeXL Network Metrics

34 NodeXL simplifies mapping data a:ributes to display a:ributes

35 NodeXL Generates Sub- Graph Images

36 NodeXL displays subgraph images along with network metadata

37 NodeXL allows for fine control over the display of the network

38 NodeXL Generates Images of Networks

39 NodeXL Generates Network Graph Images

40 NodeXL enables filtering of networks

41 NodeXL Generates Filtered Network Images

42 Analogy: Clusters Are Occluded Hard to count nodes, clusters

43 Separate Clusters Are More Comprehensible

44 TwiDer Network for MicrosoE Research

45 NodeXL Generates Overall Network Metrics

46 Social networks in TwiDer among people with at least one connecgon to someone else who Tweeted Obama on January 25, 2011

47 Network of word pairs frequently men8ons among people who Tweeted the name Obama on January 25, 2011

48 US Congressman Paul Ryan word network (January 22, 2011)

49 Congresswoman Michel Bachmann keyword network (January 25, 2011)

50 What we want to do: (Build the tools to) map the social web Move NodeXL to the web: Node for Google Doc Spreadsheets! WebGL Canvas Connect to more data sources of interest: RDF, MediaWikis, Gmail, NYT, Cita8on Networks Solve hard network manipula8on UI problems: Modal transform, Time series, Automated layouts Grow and maintain archives of social media network data sets for research use. Improve network science educa8on: Workshops on social media network analysis Live lectures and presenta8ons Videos and training materials

51 Work Items Autofill Group A:ribute Merge Edges by A:ribute Modal Transform Merge Workbooks Automated Dynamic Filters: Time Series Analysis, contrast Cap8ons and Legends Upload to Graph Gallery++: cap8ons, workbook Graph Gallery++ User Accounts, Repor8ng, RSS Feeds, Network Visualiza8on Web Canvas Import: RDF, Wiki, SharePoint, Keyword networks from text Metrics: Triad Census Layouts: Force Atlas 2, Lin Log, Bakshy Plots, Quality Measures Query- by- example search for network structures

52 How you can help Sponsor a feature Sponsor Webshop 2012 Sponsor a student Schedule training Sponsor the founda8on Donate your money, code, computa8on, storage, bandwidth, data or employee s 8me Help promote the work of the Social Media Research Founda8on

53 Contact: Marc A. Smith Chief Social Scien8st Connected Ac8on Consul8ng Group h:p:// h:p:// h:p:// h:p://delicious.com/marc_smith/paper h:p:// h:p:// h:p:// h:p:// h:p://

54 Network Maps for End Users: Collect, Analyze, Visualize and Communicate Network Insights with Zero Coding A project from the Social Media Research Founda8on: h:p://