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Testing Expansion in Bounded Degree Graphs

Testing Expansion in Bounded Degree Graphs. Artur Czumaj DIMAP (Centre for Discrete Maths and it Applications) & Department of Computer Science University of Warwick. Topic of this talk. How to distinguish good expanders from weak expanders

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Testing Expansion in Bounded Degree Graphs

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  1. Testing Expansion in Bounded Degree Graphs Artur Czumaj DIMAP(Centre for Discrete Maths and it Applications) & Department of Computer Science University of Warwick

  2. Topic of this talk • How to distinguish good expanders from weak expanders • For graphs of bounded degree, we can distinguish expanders from graphs that are “far” even from poor expanders in O(n1/2) time [in the framework of property testing] ~ • Main technical challenge: • Analysis of random walks on “non-expanders”

  3. Expanders Informally: a graph is an expander if it expands well • every set of vertices has many neighbors

  4. Testing expandersin “property testing framework” To distinguish expanders from graphs that are far from being even poor expanders: we’ll use the framework of PROPERTY TESTING

  5. Property Testing definition • Given input x • If x has the property e tester passes • If x is -far from any string that has the property etester fails • error probability < 1/3 Notion of-fardepends on the problem; Typically: one needs to changefraction of the input to obtain object satisfying the property Typically we think about as on a small constant, say, = 0.1

  6. Graph properties • Measure of being far/close from a property • Is graph connected or is far from being connected? These two graphs are close to be connected

  7. Graph properties • Measure of being far/close from a property • Is graph connected or is far from being connected? far from being connected

  8. 1st definition Graph G is -far from satisfying property P If one needs to modify more than -fraction of entries in adjacency matrix to obtain a graph satisfying P

  9. 1st definition Graph G is -far from satisfying property P If one needs to modify more than -fraction of entries in adjacency matrix to obtain a graph satisfying P ¢n2 edges have to be added/deleted Suitable for dense graphs Usually “boring” for sparse graphs

  10. 2nd definition Graph G is -far from satisfying property P If one needs to modify more than -fraction of entries in adjacency lists to obtain a graph satisfying P 1 5 2 2 1 4 5 3 3 5 2 4 2 5 1 3 2

  11. 2nd definition Graph G is -far from satisfying property P If one needs to modify more than -fraction of entries in adjacency lists to obtain a graph satisfying P Suitable for sparse graphs Main model: graphs of bounded degree

  12. Adjacency matrix model • There are very fast property testers • They’re very simple • Typical algorithm: • The analysis is (often) very hard • We understand this model very well • mostly because of very close relation to combinatorics • Select a random set of vertices U • Test the property on the subgraph induced by U

  13. General result • Every hereditary property can be tested in constant-time! • Property is hereditary if • It holds if we remove vertices [Alon & Shapira, 2003-2005]

  14. Adjacency matrix model • There are very fast property testers • They’re very simple • Typical algorithm: • The analysis is (often) very hard • We understand this model very well • mostly because of very close relation to combinatorics • Typical running time: • Select a random set of vertices U • Test the property on the subgraph induced by U

  15. What’s about adjacency lists model ? • We consider bounded-degree model • graph has maximum degree d [constant] • Much less is known • Less connection to combinatorics • Connection to random walks!

  16. Bounded-degree adjacency list model • Testing bipartitness(2-colorability) • Can be done in O(n1/2 / O(1)) time (Goldreich & Ron) ~ • Algorithm: • Select O(1/e) starting vertices • For each vertex run poly(log n/e) n1/2random walks of length poly(log n/e) • If any of the starting vertices lies on an odd-length cycle then reject • Otherwise accept

  17. Bounded-degree adjacency list model • Testing bipartitness (2-colorability) • Can be done in O(n1/2 / O(1)) time (Goldreich & Ron) • Cannot be done faster (Goldreich & Ron) • So: no constant-time algorithms ~ But we had O(1/O(1))-time tester in the adjacency matrix model For general bounded degree graphs, testing most of natural properties require superconstant-time (typically,(n1/2))

  18. This talk: testing expansion • Can we quickly test if a (bounded degree) graph has good expansion? • (n1/2) lower bound [Goldreich & Ron] • even to distinguish between a very good expander and disconnected graph with several huge components • Most property testing results in the bounded • degree model use expansion

  19. This talk: testing expansion • Can we test if a (bounded degree) graph has good expansion in O(n1/2) time? • Combinatorial expansion: • Expander = graphs without small cuts • Every vertex set U (of size at most n/2) has neighborhood of size  |U| (for certain positive constant ) • Algebraic expansion: • Expander = graph with large second largest eigenvalue

  20. Algorithm of Goldreich and Ron • Choose s = O(1/) vertices at random • For each chosen vertex v • run m = O(n1/2) random walks of length O(log n) • count the number of collisions at the end-vertices • If the number of collisions is too large then • STOP & Reject • If no STOP then • accept Random walks are on regular graphs: for each node v: choose a random neighbor with prob. 1 / 2d otherwise stay

  21. Algorithm of Goldreich and Ron • Key use of the well-known fact: • If a graph is expander then random walk of length O(log n) will reach a random vertex • If we run c n1/2 random walks (for an appropriate constant c) then we expect the number of collisions to be close to expected: ~ c2/2 • this is testing of uniform distribution 1/n( cn1/2 2 ) Key task – prove the following: If graph is -far from expander then for many starting vertices random walk won’t mix

  22. Can graphs far from expanders rapidly mix? • We don’t understand well non-expanders • We understand even less graphs that are far from expanders • Goldreich and Ron suggested algorithm • Couldn’t analyze it • Gave a conjecture – which if true – would yield property tester

  23. Testing vertex expansion • Graph G = (V,E) is an -expander if For every X 4V, |X|b|V|/2 holds: |N(X)| r  |X| • Our goal: • Distinguish graphs with vertex expansion  from those -far from having vertex expansion *, *¿ • In our case * = O(2/log n) Goldreich & Ron analyzed algebraic notion of expansion Czumaj & Sohler, FOCS’2007 Perhaps main conceptual contribution: moving from algebraic notion of expansion to the combinatorial one

  24. Algorithm of Goldreich and Ron s r16/ • Choose s = O(1/) vertices at random • For each chosen vertex v • run m = O(n1/2) random walks of length O(log n) • count the number of collisions at the end-vertices • If the number of collisions is too large then • STOP & Reject • If no STOP then • accept lr16 d2 ln(n/)/2 m r12 s n1/2/2 r(1+7) ( ) /n m 2 Easy to see: -expander will be accepted (with prob.r 0.99) Task: prove that a poor expander will be rejected

  25. Testing vertex expansion Key Property: • If G is -far from *-expander then there • is a set of vertices X 4 V such that •  |V|/4 b |X| b (1+)|V|/2 • |N(X)| b c** |X| • Think: • G is -far from *-expander • there is a large set X with b c 2|X| / log nneighbors

  26. Small ratio cut  bad mixing • Think  = (1) • What if we have set X with |N(X)| b c|X|/log n ? • Run a random walk of length < c log n/2 that starts at a random vertex from X • With a constant probability it won’t leave X !

  27. Small ratio cut  bad mixing X X has small neigborhood V - X Start random walk at a random node at V Suppose it starts at a node at X Until it’s in X, in each step it has “probability” |N(X)|/|X| of “leaving” X If random walk is shorter than |X|/|N(X)|  we don’t expect to leave X Collision probability will be large We’ll reject!

  28. Small ratio cut  bad mixing • We have a large (of size r |V|/4) set X with small neighborhood • With a constant probability a node from X will be a starting node for random walks • With a constant probability, we will have too many collisions for such a node • With a constant probability we will REJECT

  29. It suffices to prove “Key Property” Key Property: • If G is -far from *-expander then there • is a set of vertices X 4 V such that •  |V|/4 b |X| b (1+)|V|/2 • |N(X)| b c** |X|

  30. Auxiliary lemma If G=(V,E) has A 4 V with |A| b n /4 such that G[V – A] is an c*- expander then G is not -far from *-expander If G is -far from *-expander: every “small” set can be removed so that the remaining graph is still not an expander

  31. Auxiliary lemma If G=(V,E) has A 4 V with |A| b n /4 such that G[V – A] is an c*- expander then G is not -far from *-expander • We can modify • dn/2 edges in G to obtain an *-expander A V – A c  *-expander

  32. Auxiliary lemma If G=(V,E) has A 4 V with |A| b n /4 such that G[V – A] is an c*- expander then G is not -far from *-expander • We can modify • dn/2 edges in G to obtain an *-expander • Remove all edges incident to A • Add (d-1)-regular good expander in A • Remove a matching M of size |A|/2 in G[V-A] • Add arbitrary matching between A and M

  33. Proving “Key Property” If G=(V,E) has A 4 V with |A| b n /4 such that G[V – A] is an c*- expander then G is not -far from *-expander If G is -far from *-expander: every “small” set can be removed so that the remaining graph is still not an expander • Start with X = ; • G[V-A] is not an expander  • 9 A4 V-X with small neighborhood • X = A [ X • Repeat step 2 with new A until |X| r |V| /4 Proves “Key Property”

  34. Summarizing • We can distinguish between graphs (of maximum degree d) that have -vertex expansion and are -far from graph with (c2/log n)-vertex expansion in time O(d2 ln(n/) n1/2/(23))

  35. Further developments: ~ Can we distinguish (in O(n1/2) time) between graphs that have -vertex expansion and are -far from graph with /c-vertex expansion? Partial answer (Kale & Seshadhri’2007): O(n1/2)-time to distinguish between graphs of max-degree d that have -vertex expansion and those with max-degree 2d and -far from graphs with 2/c-vertex expansions ~

  36. Further developments: ~ Can we distinguish (in O(n1/2) time) between graphs that have -vertex expansion and are -far from graph with /c-vertex expansion? Similar result for algebraic definition of expansion Full answer (Kale & Seshadhri’2007 ANDNachmias & Shapira’2007): O(n1/2)-time to distinguish between graphs of max-degree d that have -vertex expansion and those with max-degree d and -far from graphs with 2/c-vertex expansions ~

  37. Key improvement • C & Sohler: • If G is -far from *-expander then there • is a set of vertices X 4 V such that •  |V|/4 b |X| b (1+)|V|/2 • |N(X)| b c** |X| More direct/tighter analysis of the random walks (via conductance) leads to the following: In the set X with small cut as defined above, for a constant fraction of vertices in X O(log n) random walks won’t mix well (intuition: random walk of O(log n) length will typically stay in X)

  38. Further developments: ~ Can we distinguish (in O(n1/2) time) between graphs that have -vertex expansion and are -far from graph with /c-vertex expansion? Similar result for algebraic definition of expansion Full answer (Kale & Seshadhri’2007 ANDNachmias & Shapira’2007): O(n1/2)-time to distinguish between graphs of max-degree d that have -vertex expansion and those with max-degree d and -far from graphs with 2/c-vertex expansions ~

  39. Open questions: Understand “non-expanding” graphs Understand random walks on “non-???” graphs • That is, on graphs that don’t satisfy certain property

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