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Large-Scale Social Network Analysis – The STACC Experience

Large-Scale Social Network Analysis – The STACC Experience. Marlon Dumas. STACC Social Network Analysis Project Research Streams. Sociall y-Sensitive Search using Landmark -based Estimation of Shortest Path Distances.

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Large-Scale Social Network Analysis – The STACC Experience

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  1. Large-Scale Social Network Analysis – The STACC Experience Marlon Dumas

  2. STACC Social Network Analysis ProjectResearch Streams

  3. Socially-Sensitive Search using Landmark-based Estimation of Shortest Path Distances Konstantin TretyakovAbel Armas-Cervantes,Luciano García-Bañuelos,JaakVilo,Marlon Dumas

  4. Socially sensitive search

  5. Socially sensitive search

  6. Contact search

  7. Socially sensitive search

  8. Socially sensitive search

  9. Socially sensitive search Naïve approach (Breadth-First-Search) requires 5-20 minutes

  10. Landmark-based estimation 1 3 4 Basic Method

  11. Landmark-based estimation

  12. Shortest path tree

  13. Least common ancestor LCA

  14. Least common ancestor Shortcutting

  15. Combining multiple landmarks 2 2 2 3

  16. Combining multiple landmarks

  17. Combining multiple landmarks Landmarks-BFS

  18. Combining multiple landmarks Landmarks-BFS Given two nodes U and V: Collect all paths from U and V to all landmarks Run a BFS* on the induced subgraph * or Dijkstra, or A*, or anything else

  19. Landmark-based approximation Basic Method LCA Speed Accuracy Shortcutting Landmarks-BFS

  20. Landmark-based approximation Basic Method LCA Shortcutting Dynamic Landmarks-BFS

  21. Results

  22. Results

  23. Results

  24. Results

  25. Results

  26. Results

  27. Outline • Improvement to Basic Landmark method • Dynamic updates • Landmark selection • Evaluation

  28. Landmark selection method • Landmark is good if it covers many shortest paths Highest degree Best coverage

  29. Best Coverage

  30. Best Coverage

  31. Best Coverage

  32. Evaluation

  33. Results

  34. Timings : Query

  35. Timings : Updates * very non-uniform

  36. Timings : Landmark selection

  37. Summary (Skype graph)

  38. Summary LCA Shortcutting Landmarks-BFS Dynamic updates Best coverage Highest degree

  39. Generalizations • To weighted graph: • Use weighted shortest path trees • The dynamic update algorithm becomes slightly more complicated • To directed graph: • Use two SPTs per landmark

  40. Improvements • “Evolutionary” on-line selection of landmarks • Use of landmark-based heuristics with A* for exact path possible (Goldberg et al., Ikeda et al.)

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