1. Measuring and Extracting Proximity in Complex Networks Emden Gansner, Yehuda Koren, Stephen North, Chris Volinsky AT&T Labs Research

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3. large social networks 31M438K co-authors 1.1M896K actor-actor 1000M300M phone calls 800M200M IM data source|V||E|

4. 18 node subgraph Proximity: 1.35e+01 Captured: 1.31e+01 (97%) Adam? Glenn? Emden? Connecting Co-Authors in DBLP

5. 95% of communication between… 5 node subgraph Proximity: 7.10e+00 Captured: 6.74e+00 (95%(

6. Our goals Measure proximity between nodes. Explain proximity by extracting connection subgraphs that are readily visualized.

7. What is proximity? proximity [prox·im·i·ty || pr ɑ k's ɪ mət ɪ /pr ɒ -]n. adjacency, nearness, closeness, vicinity Network proximity is an elusive notion! Let’s work by refining a series of definitions.

8. Measuring proximity Simplest approach – length of shortest path Easily visualized

9. Measuring proximity Simplest approach – length of shortest path Easily illustrated Disregards alternative paths Captures 56% Captures 98%

10. Measuring proximity Simplest approach – length of shortest path Easily visualized Disregards alternative paths Naïve calculation will be fooled by high degrees Example from a telephone call graph…

11. Which pair is closer? Lefty Stephen Suresh Shankar Both paths are 2-hops, about the same lengths But when considering node-degrees… Meaningful connection Random connection?

12. Measuring proximity – 2 nd try Net network flow between the nodes Accounts for multiple paths Distance indifferent – might favor long paths High degree are still an issue

13. Measuring proximity – 3 rd try Delivered electric current (effective conductance) Resistor network model Accounts for multiple paths Penalizes long paths High degrees?? Getting us closer… “intuitive” Physical analogy is not perfect! edge weights conductance, inverse-resistance 1V0V

14. When is the electrical current analogy misleading? Significant connectionNoise? What does current flow mean?

15. When is the electric current analogy misleading? Noise?Significant connection Same current flow in both cases! Degree-1 nodes are neutral (attract no flow) Degree-1 nodes are very common, due to incomplete information

16. Augment network by a universal sink [Faloutsos, McCurley & Tomkins, KDD 2004] Connect all nodes to a grounded universal sink (with 0V) Tax each node - deliver portion of the flow to the sink No internal nodes of degree 1 (above problem solved) Penalizes long paths A new parameter to worry about: Which tax system? - Constant tax? Proportional tax? Tax brackets? How much? There is a worse problem…

18. Universal sink and (non-)monotonicity In our previous notions of proximity, adding nodes/edges to the network couldn’t decrease proximity Hmmm…this “blind monotonicity” was part of their shortcoming… Network size Proximity

19. Universal sink and (non-)monotonicity For all previous measures, adding nodes/edges to the network couldn’t decrease proximity With universal sink – no monotonicity: Larger network  proximity tends to zero, sink attracts more flow Even adding s—t paths can decrease proximity! Network size Proximity

20. Universal sink and (non-)monotonicity Problems with non-monotonicity: –Counter-intuitive and hard to use –Size bias makes proximity-comparison across different pairs completely unreliable –Impossible to explain (size-dependent) proximity using a connection subgraph Network size Proximity

21. A random-walk perspective Current-flow model has a direct r.w. interpretation Reminder: We defined proximity by “delivered current” or “effective conductance” The escape probability, Pesc(s  t), is the probability that a r.w. originating at s will reach t before visiting s again Let Deg(s) be the number of r.w.’s originating at s The effective conductance between s and t, is Pesc(s  t)*Deg(s)

22. “ Dead end” paths have no influence on escape probability Both graphs have the same escape- probability from red to green Lower red  green escape probability Higher red  green escape probability In both cases higher effective conductance by Rayleigh’s Monotonicity Law

23. Extending escape probability The escape probability, Pesc(s  t), is the probability that a r.w. originating at s will reach t before visiting s again The cycle-free escape probability, Pc.f.esc(s  t) is the probability that a r.w. originating at s will reach t without visiting any node more than once Multiply by degree to get an absolute quantity (accounting for the number of "actually initiated" r.w.'s): The c.f. effective conductance between s and t is Pc.f.esc(s  t)*Deg(s)

24. Higher red  green c.f. escape probability Lower red  green c.f. escape probability The c.f. effective conductance is a good candidate proximity measure: Accounts for multiple paths Favors short paths Penalizes high-degree nodes Penalizes dead-end paths Parameter free Has the “right” monotonicity Accommodates edge directions Has a natural extension to multiple endpoints

25. Computing c.f. escape probability Unlike previous measures, exact computation is impossible Practically, we can estimate it extremely well Probability of paths declines exponentially (e.g., 100 th path is x10 6 less probable than the first one.) Estimate using the most probable paths: 

26. Finding k most probable paths For an edge u-v of weight w(u,v), define its length Edge lengths are positive Exp(- ) = Prob(path) Short path High-probable path Compute k shortest simple paths in O(k|E|log|E|) time [Katoh, Ibarki and Mine, 1982] Stop searching when probability drops below “10 -6 ” of first path

27. Extracting and explaining proximity

28. Extracting proximity Cycle free effective conductance (CFEC) depends on the full graph Find a small subgraph that captures the most proximity A tradeoff between “size” and “captured proximity”, can be expressed in alternative ways: –Extract a subgraph with at most B nodes that captures maximal CFEC Maybe with B+1 nodes we can capture much more??? –Extract a minimal-sized subgraph that captures at least P% of total CFEC Maybe we can capture (P-1)% of total CFEC with a much smaller subgraph???

29. Extracting proximity Find a small subgraph that captures most proximity Achieve an efficient balance between “size” and “proximity” by maximizing the ratio: Larger α  emphasize proximity  larger subgraph – α=0  returns only the shortest path – α= ∞  return all paths Optionally, explicitly fix lower and upper bounds on subgraph size

30. What solutions do we seek? Overlapping paths delivering the most flow

31. The path merger algorithm We already have a collection of paths Find the subset of the paths that maximizes Combine the selected paths into a “proximity subgraph” Overlapping paths are cheaper to add An NP-hard problem…

32. Optimal algorithm Scanning all subsets takes O(2 k ) time (can we do better?) A branch-and-bound pruning significantly reduces running time Huge deviations in path-quality make this approach effective e.g. often it is clear that the best-subset must contain first path(s) Prematurely terminate exponential algorithm after scanning “too many” subsets

33. Agglomerative algorithm If optimal algorithm couldn’t finish, improve current result by an agglomerative algorithm Iteratively, merge the two subsets that maximize the ratio Record the best subset discovered

34. Working with large graphs in external storage Dealing with full graph is sometimes infeasible and usually unnecessary Prior to running the algorithm, we construct a candidate graph in main memory We begin by growing increasing neighborhoods around the endpoints

35. ST

36. ST Dist(T,i)=2Dist(S,i)=2

37. ST Dist(T,i)=3Dist(S,i)=3

38. ST Dist(T,i)=4Dist(S,i)=4

39. ST Dist(T,i)=5Dist(S,i)=5 Shortest path of length 10

40. No use for low- probability paths... Paths longer than “ 24 ” unneeded! Most probable path of length 10 was found

41. ST Dist(T,i)=12Dist(S,i)=12

42. ST Dist(T,i)=12Dist(S,i)=12 i Stop adding nodes Any s—t path through unscanned node must be longer than “24”, thus useless Can we prune the resulting graph? Yes! From two circles into an ellipse…

43. Pruning the candidate graph We can safely prune a significant portion of the candidate graph Use the fact: dist(i,s)+dist(i,t)>L  all s—t paths going via i are longer than L We ignore much less probable paths  Paths longer than “24” are not interesting Take only nodes within the ellipse defined by: dist(i,s)+dist(i,t)<24

44. ST From 2-centers of circles to 2-foci of ellipse Dist(T,i)=12Dist(S,i)=12 Dist(S,i)+Dist(T,i)=24

45. Some statistics…

46. Distribution of proximities in phone-call network

48. Distribution of #hops in phone-call network

53. Summary Proposed cycle free effective conductance (CFEC) with a random walk interpretation to measure “proximity” in social networks and other ad-hoc networks Described a way of approximating CFEC Described a way of visualizing CFEC as a subgraph Extended the method to external datasets Showed empirical evidence for its utility

54. Extensions Study proximity in other kinds of networks. Extend c.f. effective conductance to: –Multiple endpoints (already demonstrated) –Directed edges (future work – use k-shortest paths in a directed graph, alg. due to Hershberger et al ).