Lynn Cherny, Ghostweather R&D LLC<br />A 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]<br />Postdoc at Indiana University ad:<br />“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. <br />
Lynn Cherny, Ghostweather R&D LLC<br />And then Moritz Stefaner posted this during the summer…<br />
How can we untangle the hairball?<br />Using social network analysis metrics and filtering the data set…<br />Lynn Cherny, Ghostweather R&D LLC<br />
Community Detection Algorithms<br />Lynn Cherny, Ghostweather R&D LLC<br />Louvain method, implemented in both Gephi and NetworkX<br />Outputs a classification for each node in the list – 1, 2, 3… corresponding to the “community” they are closest to.<br />http://en.wikipedia.org/wiki/File:Network_Community_Structure.png<br />
Summary<br />Lynn Cherny, Ghostweather R&D LLC<br />You can go from a hairball to an analysis…<br />With free tools (plus some standards or extras to make the results pretty)<br />Network analysis is another form of data literacy now – think of these methods as similar to other stats, but specific to graphs.<br />
Lynn Cherny, Ph.D.email@example.com<br />Lynn Cherny, Ghostweather R&D LLC<br />Need data help? Have a hairball? Contact me.<br />See my blog post on this for more info and links:<br />http://blogger.ghostweather.com/<br />
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