Extraction and analysis of several graph features from publicly available datasets using NetworkX.

Results, sources, draft of the paper, etc.

1* Assortativity

2* Clique number

3* Clustering

4* Density

5* Diameter

6* Edge connectivity

7* Node connectivity

8* Number of cliques

9* Number of edges

10* Number of nodes

11* Radius

12* Clustering and Transitivity

13* Betweeness centrality

14* Closeness centrality

15* Communicability centrality

16* Coreness

17* Degree centrality

18* Eccentricity

19* Number of triangles

20* Pagerank

21* Square clustering

22* Transitivity

1* Social networks: online social networks, edges represent interactions between people

2* Ground truth: ground-truth network communities in social and information networks

3* Communication: email communication networks with edges representing communication

4* Citation: nodes represent papers, edges represent citations

5* Collaboration: nodes represent scientists, edges represent collaborations (co-authoring a paper)

6* Web graphs: nodes represent webpages and edges are hyperlinks

7* Products: nodes represent products and edges link commonly co-purchased products

8* p2p: nodes represent computers and edges communication

9* Roads: nodes represent intersections and edges roads connecting the intersections

10* Autonomous systems: graphs of the Internet

11* Signed networks: networks with positive and negative edges (friend/foe, trust/distrust)

12* Location based networks: Social networks with geographic check-ins

13* Wikipedia: Talk, editing and voting data from Wikipedia

14* Bio Atlas: Food-webs selected from Ecosystem Network Analysis site and from ATLSS.

15* Bio Cellular: Substrate in cellular network of corresponding organism.

16* Bio Metabolic: Metabolic network of corresponding organism.

17* Bio Carbon: Carbon exchanges in the cypress wetlands of South Florida during the wet and dry season.

18* Bio Yeast: Protein-protein interaction network in budding yeast.

Performed using snowball sampling (choosing the sample order, i.e. number of nodes). Optimized for number of edges and multiple samplings.

Cleanse the data here.

Classify the networks with lots of machine learning techniques here.

Please drop me a line or submit a patch if you have any suggestions!

Mari Wahl @ 2014 dev.mariwahl.us