The document discusses the process of simplifying social network visualizations using various tools and statistical methods. It emphasizes the importance of network analysis as a form of data literacy and provides an overview of tools like Gephi, R, and Python for conducting such analyses. The author, Lynn Cherny, offers assistance for individuals facing challenges with complex network data.

1. Simplifying Social Network VisualizationsLynn Cherny, Ph.D.

2. Why Care? Network (data) is everywhere now…1.Lynn Cherny, Ghostweather R&D LLC

3. Lynn Cherny, Ghostweather R&D LLCA social network is a social structuremade up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige. [--Wikipedia]Postdoc at Indiana University ad:“Areas of focus will include information diffusion patterns, epidemic models for the spread of ideas, interactions between network traffic and structure dynamics, and agent-based models to explain the emergence of viral bursts of attention.

4. Lynn Cherny, Ghostweather R&D LLCAnd then Moritz Stefaner posted this during the summer…

5. How can we untangle the hairball?Using social network analysis metrics and filtering the data set…Lynn Cherny, Ghostweather R&D LLC

6. Tools I UsedLynn Cherny, Ghostweather R&D LLC(free) R(free) Python (NetworkX)(free) Gephi (www.gephi.org)Tableau DesktopExcelIllustrator and Photoshop

7. Lynn Cherny, Ghostweather R&D LLCGephi – Network vis and analysisStatistics TabColor -By…Graph PreviewLayout MethodFilter Rules

8. Calculate Stats (Gephi and NetworkX)Lynn Cherny, Ghostweather R&D LLC

9. Some Important Stats – Degree, Authority, Betweenness…Lynn Cherny, Ghostweather R&D LLC

10. R – find correlations among statsLynn Cherny, Ghostweather R&D LLC

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13. BetweennessLynn Cherny, Ghostweather R&D LLCA measure of connectedness between (sub)components of the graphhttp://en.wikipedia.org/wiki/Centrality#Betweenness_centrality

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15. Use “Degree” to scale size of node label in Gephi… still a mess! Not enough yet.Lynn Cherny, Ghostweather R&D LLC

16. Detecting Sub-Communities-and then filtering.Lynn Cherny, Ghostweather R&D LLC

17. Community Detection AlgorithmsLynn Cherny, Ghostweather R&D LLCLouvain method, implemented in both Gephi and NetworkXOutputs a classification for each node in the list – 1, 2, 3… corresponding to the “community” they are closest to.http://en.wikipedia.org/wiki/File:Network_Community_Structure.png

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20. Lynn Cherny, Ghostweather R&D LLC“The Authorities”“The Researchers”

21. SummaryLynn Cherny, Ghostweather R&D LLCYou can go from a hairball to an analysis…With free tools (plus some standards or extras to make the results pretty)Network analysis is another form of data literacy now – think of these methods as similar to other stats, but specific to graphs.

22. Lynn Cherny, Ph.D.lynn@ghostweather.comLynn Cherny, Ghostweather R&D LLCNeed data help? Have a hairball? Contact me.See my blog post on this for more info and links:http://blogger.ghostweather.com/