Physics-Inspired Algorithms and Phase Transitions in Community Detection - 2014 Symposium

Physics-Inspired Algorithms and Phase Transitions in Community Detection - 2014 Symposium

Santa Fe Institute via YouTube Direct link

Non-backtracking for trust and centrality: avoid the echo chamber

22 of 26

22 of 26

Non-backtracking for trust and centrality: avoid the echo chamber

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Physics-Inspired Algorithms and Phase Transitions in Community Detection - 2014 Symposium

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  1. 1 Intro
  2. 2 What is structure?
  3. 3 Statistical inference
  4. 4 The stochastic block model
  5. 5 Assortative and disassortative
  6. 6 Likelihood and energy
  7. 7 Statistical significance
  8. 8 What's the best labeling?
  9. 9 Belief propagation (a.k.a. the cavity method)
  10. 10 The Karate Club: leaders vs. followers
  11. 11 The Karate Club: two factions
  12. 12 Two local optima in free energy
  13. 13 Active learning: update the model as we learn more
  14. 14 The double life of Belief Propagation
  15. 15 A phase transition: detectable to undetectable communities
  16. 16 Phase transitions in semisupervised learning
  17. 17 Hierarchical clustering
  18. 18 Clustering nodes with eigenvalues
  19. 19 When does this work?
  20. 20 The non-backtracking operator
  21. 21 Comparing with standard spectral methods
  22. 22 Non-backtracking for trust and centrality: avoid the echo chamber
  23. 23 Morals
  24. 24 Physics culture meets machine learning
  25. 25 Challenges
  26. 26 Shameless Plug

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