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2010.05.28 Slide 1 ManyNets Multiple Network Analysis and Visualization Catherine Plaisant Ben Shneiderman Jennifer Golbeck – [email protected] Awalin Nabila Miguel Rios Manuel Freire
Slide 1
ManyNetsMultiple Network Analysis and Visualization
Catherine Plaisant
Ben Shneiderman
Jennifer Golbeck
Manuel Freire-Moran [email protected] 2010.05.18Awalin Nabila
Miguel Rios
Manuel Freire
2010.05.28Slide N1 1 social networkWhat about comparing thousands?
2010.05.28Slide N2
1 row = 1 networkColumns = network features (metrics, distributions)Column summaries = interactive overviews
ManyNetsSocialAction [Perer08]
2010.05.28Slide NTables are nice: people are familiar with the metaphor. Think excel, or even dedicated visualization systems like Tableau or Spotfire3
1 row = 1 networkColumns = network features (metrics, distributions)Column summaries = interactive overviews
ManyNetsSocialAction [Perer08]
2010.05.28Slide NTables are nice: people are familiar with the metaphor. Think excel, or even dedicated visualization systems like Tableau or Spotfire4
1 row = 1 networkColumns = network features (metrics, distributions)Column summaries = interactive overviews
ManyNetsSocialAction [Perer08]
2010.05.28Slide NColumns can be not only straightforward numbers (number of nodes or edges), but also sets of numbers (the distribution of node degrees). We have highlighted5
1 row = 1 networkColumns = network features (metrics, distributions)Column summaries = interactive overviews
ManyNetsSocialAction [Perer08]
2010.05.28Slide N6
Split large networks to compare partsMultiple criteria sort, Filter using custom expressionsTight coupling with node-link diagrams
e.g. all ego-networksFilmTrust [Golbeck06]
2010.05.28Slide N7
2010.05.28Slide N8row selection & column overviews
2010.05.28Slide N9
2010.05.28Slide N10
1 row = 1 networkColumns = network features (metrics, distributions)Column summaries = interactive overviewsTarget users: network analysts
ManyNets
2010.05.28Slide NTables are nice: people are familiar with the metaphor. Think excel, or even dedicated visualization systems like Tableau or Spotfire11MotivationAnalysis of separate networks: compare a set of networks
Analysis of parts of a single network: divide and conquerLocal neighborhoods (ego networks) within a social networkCompare larger neighborhoods (clusters or communities)Find prevalence of certain network motifsCompare sub-networks with certain attributes (eg.: time-slices)
Analysis of multi-modal networksHandle networks with multiple types of nodes and edgesGenerate new edges (two users are connected if)
2010.05.28Slide N12separate networks example
Facebook networks from 5 US universities, from [Traud09]
2010.05.28Slide NSpecify where this came from: full citation, and learn the details on how it was obtained
Here we can see public friendship relations in Facebook networks from 5 US universities.Drawing these networks would take a long time, as some of them have more than a hundred thousand edges. The resulting pictures would also be difficult to interpret, and even more difficult to compare.
We can see that most networks have a huge central connected component, and that there are more components in georgetown and princeton than in caltech, oclahoma or unc.
There is a surprisingly large edge density in our smallest network, caltech.
There are some pretty impressive friend collectors over at oclahoma and unc, people with over 3795 friends. Due to the structure of FB, all friendships are bidirectional.13separate networks example
Facebook networks from 5 US universities, from [Traud09]
2010.05.28Slide NSpecify where this came from: full citation, and learn the details on how it was obtained
Here we can see public friendship relations in Facebook networks from 5 US universities.Drawing these networks would take a long time, as some of them have more than a hundred thousand edges. The resulting pictures would also be difficult to interpret, and even more difficult to compare.
We can see that most networks have a huge central connected component, and that there are more components in georgetown and princeton than in caltech, oclahoma or unc.
There is a surprisingly large edge density in our smallest network, caltech.
There are some pretty impressive friend collectors over at oclahoma and unc, people with over 3795 friends. Due to the structure of FB, all friendships are bidirectional.14separate networks example
Facebook networks from 5 US universities, from [Traud09]
2010.05.28Slide NSpecify where this came from: full citation, and learn the details on how it was obtained
Here we can see public friendship relations in Facebook networks from 5 US universities.Drawing these networks would take a long time, as some of them have more than a hundred thousand edges. The resulting pictures would also be difficult to interpret, and even more difficult to compare.
We can see that most networks have a huge central connected component, and that there are more components in georgetown and princeton than in caltech, oclahoma or unc.
There is a surprisingly large edge density in our smallest network, caltech.
There are some pretty impressive friend collectors over at oclahoma and unc, people with over 3795 friends. Due to the structure of FB, all friendships are bidirectional.15
separate networks example
2010.05.28Slide NSpecify where this came from: full citation, and learn the details on how it was obtained
Here we can see public friendship relations in Facebook networks from 5 US universities.Drawing these networks would take a long time, as some of them have more than a hundred thousand edges. The resulting pictures would also be difficult to interpret, and even more difficult to compare.
We can see that most networks have a huge central connected component, and that there are more components in georgetown and princeton than in caltech, oclahoma or unc.
There is a surprisingly large edge density in our smallest network, caltech.
There are some pretty impressive friend collectors over at oclahoma and unc, people with over 3795 friends. Due to the structure of FB, all friendships are bidirectional.16
separate networks example
2010.05.28Slide NSpecify where this came from: full citation, and learn the details on how it was obtained
Here we can see public friendship relations in Facebook networks from 5 US universities.Drawing these networks would take a long time, as some of them have more than a hundred thousand edges. The resulting pictures would also be difficult to interpret, and even more difficult to compare.
We can see that most networks have a huge central connected component, and that there are more components in georgetown and princeton than in caltech, oclahoma or unc.
There is a surprisingly large edge density in our smallest network, caltech.
There are some pretty impressive friend collectors over at oclahoma and unc, people with over 3795 friends. Due to the structure of FB, all friendships are bidirectional.17MotivationAnalysis of separate networks: compare a set of networks
Analysis of parts of a single network: divide and conquerLocal neighborhoods (ego networks) within a social networkCompare larger neighborhoods (clusters or communities)Find prevalence of certain network motifsCompare sub-networks with certain attributes (e.g.: time-slices)
Analysis of multi-modal networksHandle networks with multiple types of nodes and edgesGenerate new edges (two users are connected if)
2010.05.28Slide N18single network example
Nodes are users
Links are trust ratings in other users film rating expertiseJoeMary10PeterPaul82MarkTim89Ed?FilmTrust [Golbeck06]
2010.05.28Slide N19Sure, but what is filmTrust, ans what is it all aboutMarkego network radius 0
2010.05.28Slide N20ego network radius 1MarkTimEdJoeMaryPeterPaul
2010.05.28Slide N21ego network radius 1.5MarkTimEdJoeMaryPeterPaul
2010.05.28Slide N22ego network radius 2MarkTimEdJoeMaryPeterPaulLizBenJaneBethTom
2010.05.28Slide N23Q: 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)
2010.05.28Slide N24
2010.05.28Slide N25are 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)
2010.05.28Slide N26MotivationAnalysis of separate networks: compare a set of networks
Analysis of parts of a single network: divide and conquerLocal neighborhoods (ego networks) within a social networkCompare larger neighborhoods (clusters or communities)Find prevalence of certain network motifsCompare sub-networks with certain attributes (eg.: time-slices)
Analysis of multi-modal networksHandle networks with multiple types of nodes and edgesGenerate new edges (two users are connected if)
2010.05.28Slide N27multi-modal network exampleBobAliceJawsStar-warstrust = 8/10rating = 4/5rating = 3/5rating = 2/5
2010.05.28Slide N28InterfaceSupport for multi-modal networksSchemasTable levelsColumns (network metrics, features) can be removed, rearranged, addedFrom menuVia user-specified expressionFilter and sortDetails on demand in side-pane, tooltipsCreate new relationships, access the overall schema
2010.05.28Slide N29schemasBobAliceJawsStar-warstrust = 8/10rating = 4/5rating = 3/5rating = 2/5userfilmtrustratingFilmTrust Schema
2010.05.28Slide N30
userfilmtrustrating
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userfilmtrustrating
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userfilmtrustrating
2010.05.28Slide N33
userfilmtrustrating
2010.05.28Slide N34multiple node and edge types: levelsLowest level: entity and relationship tablesEntities are stand-alone, can be used as nodesRelationships relate two entities, map to edgesInside a network: node and edge tablesNodes come from entitiesEdges come from relationshipsCan mix multiple entities, relationships in a network: multi-relational or multi-modalNetwork tablesEach row is a network
2010.05.28Slide N35InterfaceSupport for multi-modal networksSchemasTable levelsColumns (network metrics, features) can be removed, rearranged, addedFrom menuVia user-specified expressionFilter and sort tableDetails on demand in side-pane, tooltipsCreate new relationships, access the overall schema
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2010.05.28Slide N37
2010.05.28Slide N38InterfaceSupport for multi-modal networksSchemasTable levelsColumns (network metrics, features) can be removed, rearranged, addedFrom menuVia user-specified expressionFilter and sortDetails on demand in side-pane, tooltipsAdvanced column overviewsCreate new relationships, access the overall schema
2010.05.28Slide N39Overviews of Distribution ColumnsManyNets Overviews [Sopan10 / under review]
2010.05.28Slide N40Overviews of Distribution Columns
ManyNets Overviews [Sopan10 / under review]
2010.05.28Slide N41InterfaceSupport for multi-modal networksSchemasTable levelsColumns (network metrics, features) can be removed, rearranged, addedFrom menuVia user-specified expressionFilter and sort tableDetails on demand in side-pane, tooltipsCreate new relationships, access the schema
2010.05.28Slide N42Deriving new relationshipsBobAliceJawsStar-warstrust = 8/10rating = 4/5rating = 3/5rating = 2/5userfilmtrustratingFilmTrust Schema
2010.05.28Slide N43Deriving new relationshipsBobAliceJawsStar-warstrust = 8/10rating = 4/5rating = 3/5rating = 2/5userfilmtrustratingExtended SchemaCo-ratedweight = 1
2010.05.28Slide N44Deriving new relationshipsBuild new relationships on the flyExtend schema with each relationshipRetain access to original dataCompare resulting networks to each otheruserfilmtrustratingCo-ratedGood predictor for
2010.05.28Slide N45ValidationOriginal ManyNets (presented at CHI 2010)Case Study on FilmTrust with domain expertFormative usability test (7 users)
ManyNets2 (work in progress)NSF grant dataYour dataset here!
2010.05.28Slide N46ConclussionMultimodal network analysis is hardManyNets can help!build and explore sets of networkssplit, filter, rank, overview, drill, elide, synthesizeReveals patterns within network attributesDoes 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)www.cs.umd.edu/projects/hcil/manynets/
Acknowledgements Partial support from Lockheed Martin Manuel Freire supported by Fulbright Scholarship2010.05.28Slide N47
