Finding sparse cuts locally using evolving sets

A local graph partitioning algorithm finds a set of vertices with small conductance (i.e.~a sparse cut) by adaptively exploring a large graph G, starting from a specified vertex. For the algorithm to be local, its complexity must be bounded in terms of the size of the set it outputs, with at most a weak dependence on n, the number of vertices in G. Previous local partitioning algorithms find sparse cuts using random walks and personalized PageRank. In this paper, we introduce a randomized local partitioning algorithm that finds a sparse cut by simulating the volume-biased evolving set process, which is a Markov chain on sets of vertices. We prove that for any set of vertices A that has conductance at most φ, and for at least half of the starting vertices in A, our algorithm will output (with probability at least half) a set of conductance O(φ1/2 log1/2 n). The complexity of a local partitioning algorithm is measured by its work/volume ratio, which is the ratio between the computational complexity of the algorithm on a given run, and the volume of the set output. We prove that for our algorithm, the expected value of the work/volume ratio is polylognoparen(φ-1/2). The best previous local partitioning algorithm, due to Andersen, Chung, and Lang, has the same approximation guarantee but a larger work/volume ratio of polylognoparen(φ-1). As an application of our local partitioning algorithm, we construct a fast algorithm for finding balanced cuts. The resulting algorithm takes as input a graph and a fixed value of φ, has complexity polylog{m+nφ-1/2), and returns a cut with conductance O(φ1/2 log1/2 n) and volume at least vφ/2, where vφ is the volume of the largest set in the graph with conductance at most φ.

[1]  RAVI MONTENEGRO SHARP EDGE , VERTEX , AND MIXED CHEEGER INEQUALITIES FOR FINITE MARKOV KERNELS , 2007 .

[2]  U. Feige,et al.  Spectral Graph Theory , 2015 .

[3]  Jure Leskovec,et al.  Statistical properties of community structure in large social and information networks , 2008, WWW.

[4]  Yuval Peres,et al.  Evolving sets and mixing , 2003, STOC '03.

[5]  Satish Rao,et al.  Expander flows, geometric embeddings and graph partitioning , 2004, STOC '04.

[6]  Ronald L. Rivest,et al.  Introduction to Algorithms, Second Edition , 2001 .

[7]  Shang-Hua Teng,et al.  Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems , 2003, STOC '04.

[8]  P. Diaconis,et al.  Strong Stationary Times Via a New Form of Duality , 1990 .

[9]  Milena Mihail,et al.  Conductance and convergence of Markov chains-a combinatorial treatment of expanders , 1989, 30th Annual Symposium on Foundations of Computer Science.

[10]  Sanjeev Arora,et al.  O( p logn) Approximation to Sparsest Cut in O(n2) Time , 2004, FOCS 2004.

[11]  Shang-Hua Teng,et al.  Spectral partitioning works: planar graphs and finite element meshes , 1996, Proceedings of 37th Conference on Foundations of Computer Science.

[12]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[13]  Sanjeev Arora,et al.  O(/spl radic/log n) approximation to SPARSEST CUT in O/spl tilde/(n/sup 2/) time , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[14]  Ravi Montenegro Sharp edge, vertex, and mixed Cheeger type inequalities for finite Markov kernels , 2006 .

[15]  Kevin J. Lang,et al.  Communities from seed sets , 2006, WWW '06.

[16]  Elad Hazan,et al.  O(/spl radic/log n) approximation to SPARSEST CUT in O/spl tilde/(n/sup 2/) time , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.

[17]  David R. Karger,et al.  Approximating s – t Minimum Cuts in ~ O(n 2 ) Time , 2007 .

[18]  David Williams,et al.  Probability with Martingales , 1991, Cambridge mathematical textbooks.

[19]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[20]  Nisheeth K. Vishnoi,et al.  On partitioning graphs via single commodity flows , 2008, STOC.

[21]  Elad Hazan,et al.  O(sqrt(log(n)) Approximation to SPARSEST CUT in Õ(n2) Time , 2004, SIAM J. Comput..

[22]  Satish Rao,et al.  Graph partitioning using single commodity flows , 2006, STOC '06.

[23]  Ravi Montenegro The simple random walk and max-degree walk on a directed graph , 2009, Random Struct. Algorithms.

[24]  C. Lee Giles,et al.  Efficient identification of Web communities , 2000, KDD '00.

[25]  Andrew V. Goldberg,et al.  Beyond the flow decomposition barrier , 1998, JACM.

[26]  Fan Chung Graham,et al.  Local Graph Partitioning using PageRank Vectors , 2006, 2006 47th Annual IEEE Symposium on Foundations of Computer Science (FOCS'06).

[27]  Sanjeev Arora,et al.  A combinatorial, primal-dual approach to semidefinite programs , 2007, STOC '07.