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Layout Algorithm for ClusteredGraphs to Analyze CommunityInteractions in Social NetworksJuan David CruzCécileBothorelFranç...
Institut Mines-TélécomIntroduction – IReal world social networks store bothsocial and structural information fromthe actor...
Institut Mines-TélécomIntroduction – II? How to represent these structural and profile similarities on the sameplane while...
Institut Mines-TélécomLayout of communities – Objective? How to represent these structural and profile similarities on the...
Institut Mines-TélécomBibliographical revision – Clustered graphslayout5 Juan David CruzForce based models Hierarchical mo...
Institut Mines-TélécomBibliographical revision – Clustered graphslayout - Examples6 Juan David CruzMultilevel (Eades&Feng)...
Institut Mines-TélécomClustered graphs layout algorithms -SummaryInformation usedMethod StructuralProfiles CommunitiesMult...
Institut Mines-TélécomVisualization of communities The algorithm allows correlating structural and profileinformation Ea...
Institut Mines-TélécomVisualization of communities – Multi-Dimensional Scaling Maps a similarity into a 2/3dimensional sp...
Institut Mines-TélécomVisualization of communities – Types ofnodes10 Juan David CruzThese are the nodes connectingcommunit...
Institut Mines-TélécomVisualization of communities – Thealgorithm1. For each node set a dissimilarity matrix is calculated...
Institut Mines-TélécomVisualization of communities –Experiments – Setup The goal of the experiments is to test the algori...
Institut Mines-TélécomVisualization of communities –Experiments – Setup: roles13Roles from Guimera and Amaral, 2005Juan Da...
Institut Mines-TélécomVisualization of communities –Experiments – FacebookAmbassadors help their communities to get into t...
Institut Mines-TélécomVisualization of communities –Experiments – DBLPIn this graph, several well connected nodes remain a...
Institut Mines-TélécomVisualization of communities –Experiments – Protein interactionWith our algorithm it is possible to ...
Institut Mines-TélécomVisualization of communities – Complexity17 Juan David CruzComplexity of the algorithmThe overall co...
Institut Mines-TélécomTable of contents18 Juan David Cruz3 Conclusion and perspectives
Institut Mines-TélécomConclusion and perspectives Our proposed visualization model focuses on the integration ofthe varia...
Institut Mines-TélécomConclusion and perspectives – Future work The visual model can be extended to include the notion of...
Institut Mines-TélécomThank you for your attentionDo you have questions?21 Juan David Cruz
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Sunbelt 2013 Presentation

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Sunbelt 2013 Presentation

  1. 1. Layout Algorithm for ClusteredGraphs to Analyze CommunityInteractions in Social NetworksJuan David CruzCécileBothorelFrançois PouletSUNBELT Conference 2013May 23rd, 2013, Hamburg, Germany
  2. 2. Institut Mines-TélécomIntroduction – IReal world social networks store bothsocial and structural information fromthe actorsFor example, this social network fromFacebook, contains actors’personalinformation and the links betweenthem…This social network is described by twotypes of information, which isintegrated into the communities thatcan be identified…2 Juan David Cruz
  3. 3. Institut Mines-TélécomIntroduction – II? How to represent these structural and profile similarities on the sameplane while presenting the communities configuration?Combining both types of information helpsto identify groups of similar and wellconnected nodes.-Find groups of friends who are similarfrom a point of view of their hobbies-Find groups of friends from a point ofview of their academic competencesThese new partitions can be analyzedusing visual analytics approaches… buthow to use a visual approach to exploit allthis information?3 Juan David Cruz
  4. 4. Institut Mines-TélécomLayout of communities – Objective? How to represent these structural and profile similarities on the sameplane while presenting the communities configuration?-Graph layout has several challenges, fromcomputational complexity to readability-These challenges are true for visualizingand analyzing communitiesWe want to:-Reduce the node cluttering while showingthe relationships between profile andstructure in the communities-Observe interactions betweencommunities and find important nodes4 Juan David Cruz
  5. 5. Institut Mines-TélécomBibliographical revision – Clustered graphslayout5 Juan David CruzForce based models Hierarchical models Other modelsPartition of the graph GGraph G-Multilevel-LinLog-Multilevel for weightedgraphs-Kamada-Kawai based-Hierarchic quotientgraph-Radial hierarchyrepresentation-Hierarchical visualclustering-Rectangles andstraight lines-Topological features-Overlapping clusteredgraphs! In general, these models are oriented to differentiate the groups, separatingthem from each other: establish their limits.
  6. 6. Institut Mines-TélécomBibliographical revision – Clustered graphslayout - Examples6 Juan David CruzMultilevel (Eades&Feng) Orthogonal (di Battista et al.)Weighted Multilevel (Bourqui et al.) Overlapping (Santamaría&Therón)
  7. 7. Institut Mines-TélécomClustered graphs layout algorithms -SummaryInformation usedMethod StructuralProfiles CommunitiesMultilevel /Force directed [Eades&Feng 1999] No No YesRectangles and straight lines [diGiacomo 2007 ] No No YesLinLog /Force model [Noack 2003] Yes No NoHierarchical /Quotient graph [Brockenauer 2001] No No YesMultilevel /Force directed w. graphs [Bourqui et al.2007]Yes No YesOverlapping clustered graphs [Santamaría et al. 2008] No No YesRadial representation [Mun& Ha 2005] Yes No NoHierarchical /visual clustering [Batagelj et al. 2011] Yes No NoKamada-Kawai based [Shi et al. 2009] No No YesTopological features based [Archambault et al. 2007] Yes No YesMultivariate layout algorithm (My algorithm) Yes Yes Yes7 Juan David Cruz
  8. 8. Institut Mines-TélécomVisualization of communities The algorithm allows correlating structural and profileinformation Each group has diverse categories from the profileinformation The algorithm focuses on individuals connectingcommunities (boundary connectors) Describe boundary connectors with their profile andtheir neighborhood8 Juan David Cruz
  9. 9. Institut Mines-TélécomVisualization of communities – Multi-Dimensional Scaling Maps a similarity into a 2/3dimensional space MDS uses a (dis)similarity matrixas input The output is a set of coordinateswhose distances resemble the(dis)similarities (Dis)similarities:• Geographic distances• Jaccard distance (vectors, sets)• Geodesic distances (graphs)9 Juan David CruzDissimilarity matrix2D Coordinates
  10. 10. Institut Mines-TélécomVisualization of communities – Types ofnodes10 Juan David CruzThese are the nodes connectingcommunities: have neighbors in otherclusters, defining the interaction zone.These are the nodes with edges from/tonodes in the same cluster only. Placedoutside the interaction zone.Border nodesInner nodes
  11. 11. Institut Mines-TélécomVisualization of communities – Thealgorithm1. For each node set a dissimilarity matrix is calculated using the profileand the structural information2. The coordinates reflect the proximity of the nodes in terms of the twovariables – Output of the MDS algorithm3. The final coordinates transformation defines the interaction zone11 Juan David Cruz
  12. 12. Institut Mines-TélécomVisualization of communities –Experiments – Setup The goal of the experiments is to test the algorithm capabilities ofidentifying important nodes regarding the connections and insideconnections The graphs used in experimentation has a low edge density, expecting tohave a community structure The community structure makes these graphs suitable for our algorithm12 Juan David CruzClustered graphs used for the layout algorithm testing
  13. 13. Institut Mines-TélécomVisualization of communities –Experiments – Setup: roles13Roles from Guimera and Amaral, 2005Juan David Cruz
  14. 14. Institut Mines-TélécomVisualization of communities –Experiments – FacebookAmbassadors help their communities to get into the interaction zone (where thecommunities interact.) The influence of the structural similarity is reflected on theproximity of the nodes14 Juan David CruzLayout using Fruchterman&Reingold Layout using our algorithmInteractionzoneInnernodes
  15. 15. Institut Mines-TélécomVisualization of communities –Experiments – DBLPIn this graph, several well connected nodes remain as inner nodes. These nodes canbe seen as gurus in their communities, however they are not connected with othercommunities (treating other topics)15 Juan David CruzLayout using Fruchterman&Reingold Layout using our algorithm
  16. 16. Institut Mines-TélécomVisualization of communities –Experiments – Protein interactionWith our algorithm it is possible to observe the sizes of the inner nodes and to identifythose nodes important in regard of the interactions. However, this representation hasto be analyzed by an expert to give some insight about the configuration16 Juan David CruzLayout using Fruchterman&Reingold Layout using our algorithm
  17. 17. Institut Mines-TélécomVisualization of communities – Complexity17 Juan David CruzComplexity of the algorithmThe overall complexity of the algorithm is:The algorithm was implemented using twoparallelization approaches: threaded CBLASroutines and GP-GPU CUBLAS routineswhere available…Results of the experimentsIn general the complexity is quadratic infunction of the number of border nodesGraph % border nodes Time(s)Protein interaction 38% 1021DBLP network 40% 346Twitter network 24% 89Facebook network 25% 36
  18. 18. Institut Mines-TélécomTable of contents18 Juan David Cruz3 Conclusion and perspectives
  19. 19. Institut Mines-TélécomConclusion and perspectives Our proposed visualization model focuses on the integration ofthe variables existing on a social network Dividing the nodes into two categories allows identifyingimportant nodes regarding the communication betweencommunities This division reduces the complexity (in average) of the layoutalgorithm The nodes are placed in such way the distance between themrepresents their structural similarity The model was implemented using PT-CBLAS and CUBLAS toimprove some operations of the algorithm (parallelization) Qualitative studies have to be performed to test the functionalityof the model on real research cases19 Juan David Cruz
  20. 20. Institut Mines-TélécomConclusion and perspectives – Future work The visual model can be extended to include the notion of point ofview, showing the impact of selecting different elements from theprofile information Use this visualization method on real world applications such asidentification of influencing actors in marketing campaigns20 Juan David Cruz
  21. 21. Institut Mines-TélécomThank you for your attentionDo you have questions?21 Juan David Cruz

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