Slide 1 ManyNets Multiple Network Analysis and Visualization Catherine Plaisant Ben Shneiderman Jennifer Golbeck Manuel Freire-Moran – Awalin Nabila Miguel Rios Manuel Freire

Slide 2 1 social network What about comparing thousands?

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

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

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

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

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

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Slide 9 row selection & column overviews

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

Slide 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…”)

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

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

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

Slide 16 separate networks example

Slide 17 separate networks example

Slide 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…”)

Slide 19 single network example Nodes are users Links are trust ratings in other users’ film –rating expertise JoeMary 10 PeterPaul 8 2 Mark Tim 8 9 Ed ? FilmTrust [Golbeck06]

Slide 20 Mark ego network – radius 0

Slide 21 ego network – radius 1 Mark TimEd Joe Mary Peter Paul

Slide 22 ego network – radius 1.5 Mark TimEd Joe Mary Peter Paul

Slide 23 ego network – radius 2 Mark TimEd Joe Mary Peter Paul Liz Ben Jane Beth Tom

Slide 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)

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Slide 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)

Slide 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…”)

Slide 28 multi-modal network example BobAlice Jaws Star-wars trust = 8/10 rating = 4/5 rating = 3/5 rating = 2/5

Slide 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

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

Slide 31 user film trust rating

Slide 32 user film trust rating

Slide 33 user film trust rating

Slide 34 user film trust rating

Slide 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

Slide 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

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Slide 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, tooltips Advanced column overviews Create new relationships, access the overall schema

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

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

Slide 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

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

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

Slide 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 user film trust rating Co-rated Good predictor for

Slide 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!

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