ManyNets Multiple Network Analysis and Visualization

1. ManyNets Multiple Network Analysis and Visualization Awalin Nabila Miguel Rios Manuel Freire Catherine Plaisant Jennifer Golbeck Ben Shneiderman Manuel Freire-Moran – manuel.freire@uam.es 2010.05.18

2. 1 social network What about comparing thousands?

3. ManyNets 1 row = 1 network Columns = network features (metrics, distributions) Column summaries = interactive overviews SocialAction [Perer08]

4. ManyNets 1 row = 1 network Columns = network features (metrics, distributions) Column summaries = interactive overviews SocialAction [Perer08]

5. ManyNets 1 row = 1 network Columns = network features (metrics, distributions) Column summaries = interactive overviews SocialAction [Perer08]

6. ManyNets 1 row = 1 network Columns = network features (metrics, distributions) Column summaries = interactive overviews SocialAction [Perer08]

7. Splitlarge networks to compare parts e.g. all ego-networks FilmTrust[Golbeck06] Multiple criteria sort, Filter using custom expressions Tight coupling with node-link diagrams

9. row selection & column overviews

11. ManyNets 1 row = 1 network Columns = network features (metrics, distributions) Column summaries = interactive overviews Target users: network analysts

12. Motivation • Analysis of separate networks: compare a set of networks • Analysis of parts of a single network: divide and conquer • Local neighborhoods (ego networks) within a social network • Compare larger neighborhoods (clusters or communities) • Find prevalence of certain network motifs • Compare sub-networks with certain attributes (eg.: time-slices) • Analysis of multi-modal networks • Handle networks with multiple types of nodes and edges • Generate new edges (“two users are connected if…”)

13. separate networks example Facebook networks from 5 US universities, from [Traud09]

14. separate networks example Facebook networks from 5 US universities, from [Traud09]

15. separate networks example Facebook networks from 5 US universities, from [Traud09]

16. separate networks example

17. separate networks example

18. Motivation • Analysis of separate networks: compare a set of networks • Analysis of parts of a single network: divide and conquer • Local neighborhoods (ego networks) within a social network • Compare larger neighborhoods (clusters or communities) • Find prevalence of certain network motifs • Compare sub-networks with certain attributes (e.g.: time-slices) • Analysis of multi-modal networks • Handle networks with multiple types of nodes and edges • Generate new edges (“two users are connected if…”)

19. single network example Nodes are users Links are trust ratings in other users’ film –rating expertise 10 Joe Mary 8 Peter Paul 2 Mark 9 8 Ed Tim FilmTrust[Golbeck06] ?

20. ego network – radius 0 Mark

21. ego network – radius 1 Mary Peter Joe Paul Mark Ed Tim

22. ego network – radius 1.5 Mary Peter Joe Paul Mark Ed Tim

23. ego network – radius 2 Jane Mary Peter Joe Paul Mark Ed Tim Tom Liz Ben Beth

24. Q: are big ego nets similar to small ones? picture of trust distribution in big ego nets (large neighborhood) picture of trust distribution in small ego nets (small neighborhood)

26. are big ego nets similar to small ones? picture of trust distribution in big ego nets (large neighborhood) picture of trust distribution in small ego nets (small neighborhood)

27. Motivation • Analysis of separate networks: compare a set of networks • Analysis of parts of a single network: divide and conquer • Local neighborhoods (ego networks) within a social network • Compare larger neighborhoods (clusters or communities) • Find prevalence of certain network motifs • Compare sub-networks with certain attributes (eg.: time-slices) • Analysis of multi-modal networks • Handle networks with multiple types of nodes and edges • Generate new edges (“two users are connected if…”)

28. multi-modal network example trust = 8/10 Alice Bob rating = 2/5 rating = 4/5 Jaws rating = 3/5 Star-wars

29. Interface • Support for multi-modal networks • Schemas • Table levels • Columns (network metrics, features) can be removed, rearranged, added • From menu • Via user-specified expression • Filter and sort • Details on demand in side-pane, tooltips • Create new relationships, access the overall schema

30. schemas trust = 8/10 trust Alice Bob user rating = 2/5 rating = 4/5 rating Jaws rating = 3/5 film Star-wars FilmTrust Schema

31. trust user rating film

32. trust user rating film

33. trust user rating film

34. trust user rating film

35. multiple node and edge types: levels • Lowest level: entity and relationship tables • Entities are stand-alone, can be used as nodes • Relationships relate two entities, map to edges • Inside a network: node and edge tables • Nodes come from entities • Edges come from relationships • Can mix multiple entities, relationships in a network: multi-relational or multi-modal • Network tables • Each row is a network

36. Interface • Support for multi-modal networks • Schemas • Table levels • Columns (network metrics, features) can be removed, rearranged, added • From menu • Via user-specified expression • Filter and sort table • Details on demand in side-pane, tooltips • Create new relationships, access the overall schema

39. Interface • Support for multi-modal networks • Schemas • Table levels • Columns (network metrics, features) can be removed, rearranged, added • From menu • Via user-specified expression • Filter and sort • Details on demand in side-pane, tooltipsAdvanced column overviews • Create new relationships, access the overall schema

40. Overviews of Distribution Columns ManyNets Overviews [Sopan10 / under review]

41. Overviews of Distribution Columns ManyNets Overviews [Sopan10 / under review]

42. Interface • Support for multi-modal networks • Schemas • Table levels • Columns (network metrics, features) can be removed, rearranged, added • From menu • Via user-specified expression • Filter and sort table • Details on demand in side-pane, tooltips • Create new relationships, access the schema

43. Deriving new relationships trust = 8/10 trust Alice Bob user rating = 2/5 rating = 4/5 rating Jaws rating = 3/5 film Star-wars FilmTrust Schema

44. Deriving new relationships trust = 8/10 trust Co-rated weight = 1 Alice Bob user rating = 2/5 rating = 4/5 rating Jaws rating = 3/5 film Star-wars Extended Schema

45. Deriving new relationships • Build new relationships on the fly • Extend schema with each relationship • Retain access to original data • Compare resulting networks to each other trust Co-rated user Good predictor for rating film

46. Validation • Original ManyNets (presented at CHI 2010) • Case Study on FilmTrust with domain expert • Formative usability test (7 users) • ManyNets2 (work in progress) • NSF grant data • Your dataset here!

47. Conclussion • Development page (application, datasets, manual) • tangow.ii.uam.es/mn/ • open-source, feedback welcome! (please contact us) • Academic page (publications, demo videos) • www.cs.umd.edu/projects/hcil/manynets/ • Acknowledgements • Partial support from Lockheed Martin • Manuel Freire supported by Fulbright Scholarship • Multimodal network analysis is hard • ManyNets can help! • build and explore sets of networkssplit, filter, rank, overview, drill, elide, synthesize… • Reveals patterns within network attributes • Does so interactively, allowing exploratory search