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Networkx & Gephi Tutorial #Pydata NYC
The document discusses network analysis using tools like NetworkX and Gephi, focusing on graph properties such as indegree, outdegree, and centrality measures. It highlights various graph types and their characteristics, including complete graphs, bipartite graphs, and small-world networks, using social networks as examples. Additionally, it provides insights into Twitter data collection and analysis related to the 'data science' community on the platform.
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Networkx & Gephi Tutorial #Pydata NYC
- 1. Networkx & GephiTutorial #pydata Gilad Lotan | @gilgul
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- 6. #gayrights, #lgbt, #jesus, #palestine, #OWS, #immigration, #flipflop, #jobs, #economy #abortion #republican, #dems, #economics, #amnesty
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- 8. #Debates / Ohio Politicos Ohio based Media OSU Students
- 9. • Node networkproperties – from immediate connections indegree=3 • indegree how many directed edges (arcs) are incident on a node outdegree=2 • outdegree how many directed edges (arcs) originate at a node degree=5 • degree (in or out) number of edges incident on a node – from the entire graph • centrality (betweenness, closeness) Source: Lada Adamic (SI508-F08)
- 10. Example Graph Types •Complete Graph • Bipartite Graph – Vertices can be divided into two disjoint sets – Ex: students & schools
- 12. Social Network Attributes •Scale Free – Degree distribution follows a power law – Barabasi et al (‘99): mapped the topology of a portion of the web • Small World – Most nodes are not neighbors, but can be reached by small number of hops – Watts & Strogatz (’98) – Properties: cliques, sub networks with high clustering coefficient, most pairs of nodes connected by at least one short path
- 13. (Zachary) Karate clubgraph social network of friendships between 34 members of a karate club at a US university in the 1970s. Standard test network for clustering algorithms -> during the observation period the club broke up into two separate clubs over a conflict.
- 14. Graph Measures • Centrality – Betweenness – Closeness – Eigenvector – Degree • Clustering Coefficient (clique) • Modularity
- 15. Graph Layout • OpenOrd – Better distinguishes clusters • Yifan Hu • Force Atlas • Fruchterman Reingold – Graph as a system of mass particles (nodes:particles, edges:springs)
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- 20. graphml file nodes edges
- 21. Twitter Users withPython in their Bios • 2 days of Twitter data (Oct 24th and 25th) • Total: 4246 users (62k tweets) • @mikanyan1 tweeted 795 times
- 22. Pythonistas on Twitter
- 23. Pythonistas on Twitter Spanish Speakers English / European Chinese Python (the snake) Japanese Musicians, Artists
- 25. Twitter User Community:Data Science • Grepped from Twitter bios over 1 week: "data science|data scientist|machine learning|data strateg” • 1053 Users • 14k Tweets • Most tweeting users: – @data_nerd (659) – @Chantel_Esworth (562) – @Da5_12 (253)
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- 27. Thank You Gilad Lotan Twitter: @gilgul Github: giladlotan
Editor's Notes
- #3 Homophily
- #4 Endogenous Trend – information spread
- #5 Exogenous information spread
- #6 Hashtags have emerged as a way for people to gather around topics or events.
- #7 - Mitt romney: #gayrights, #lgbt, #jesus, #flipflop, #jobs, #economy- Newt Gingrich: #palestine, #OWS, #immigration, #abortion (he famously said – “Stop whining, take a bath and get a job!”Equal: #republican, #dems, #economics, #amnestyCo-occurence
- #12 Networkx supports
- #14 Zachary's Karate Club Graph describes the friendships between the members of a US karate club in the 1970s. The significant feature of this social network is that the club president and the instructor were involved in a dispute (some might say: a fight) over the issue of how much to charge for lessons. This split the club into two factions, one centred around the president, and the other centred around the instructor.
- #15 Betweenness – number of shortest paths from all vertices that pass through that node / positioningCloseness – how fast it will take to spread information from s to all other nodes sequentially / distance of s from all other actors in a networkEigenvector – measure of the influence of a node (page rank, connections to high scoring nodes contribute more to the score)Clustering Coefficient – measure of degree to which nodes in a graph tend to cluster together (how close to being a clique = 1)
- #17 NetworkX is a Python language software package for the creation, manipulation, and study of the structure, dynamics, and function of complex networks.NetworkX was born in May 2002. The original version was designed and written by AricHagberg, Dan Schult, and Pieter Swart in 2002 and 2003. The first public release was in April 2005.
- #22 Python – user description2 days of Twitter data-