Local Graph Partitioning using PageRank Vectors
University of California, San Diego · University of California, Berkeley · +1 more institution
Abstract
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 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(radic(Phi…
Citation impact
- FWCI
- 25.16
- Percentile
- 100%
- References
- 25
Authors
3Topics & keywords
- PageRank
- Vertex (graph theory)
- Combinatorics
- Random walk
- Set (abstract data type)
- Graph
- Conductance
- Distribution (mathematics)