Translator Disclaimer
2007 Using PageRank to Locally Partition a Graph
Reid Andersen, Fan Chung, Kevin Lang
Internet Math. 4(1): 35-64 (2007).


A local graph partitioning algorithm finds a cut near a specified starting vertex, with a running time that depends largely on the size of the small side of the cut, rather than the size of the input graph. In this paper, we present a local partitioning algorithm using a variation of PageRank with a specified starting distribution. We derive a mixing result for PageRank vectors similar to that for random walks, and we show that the ordering of the vertices produced by a PageRank vector reveals a cut with small conductance. In particular, we show that for any set $C$ with conductance $\Phi$ and volume $k$, a PageRank vector with a certain starting distribution can be used to produce a set with conductance $O(\sqrt{\Phi \log k})$. We present an improved algorithm for computing approximate PageRank vectors, which allows us to find such a set in time proportional to its size. In particular, we can find a cut with conductance at most $\phi$, whose small side has volume at least $2^{b}$, in time $O(2^{b} \log^2m/\phi^2)$ where $m$ is the number of edges in the graph. By combining small sets found by this local partitioning algorithm, we obtain a cut with conductance $\phi$ and approximately optimal balance in time $O(m \log^4 m/\phi^2)$.


Download Citation

Reid Andersen. Fan Chung. Kevin Lang. "Using PageRank to Locally Partition a Graph." Internet Math. 4 (1) 35 - 64, 2007.


Published: 2007
First available in Project Euclid: 27 May 2009

zbMATH: 1170.68302
MathSciNet: MR2492174

Rights: Copyright © 2007 A K Peters, Ltd.


Vol.4 • No. 1 • 2007
Back to Top