Parallel Local Graph Clustering

TitleParallel Local Graph Clustering
Publication TypeConference Paper
Year of Publication2016
AuthorsShun, J., Roosta-Khorasani F., Fountoulakis K., & Mahoney M. W.
Published inProceedings of the VLDB Endowment

Graph clustering has many important applications in computing, but due to growing sizes of graph, even traditionally fast clustering methods such as spectral partitioning can be computationally expensive for real-world graphs of interest. Motivated partly by this, so-called local algorithms for graph clustering have received significant interest due to the fact that they can find good clusters in a graph with work proportional to the size of the cluster rather than that of the entire graph. This feature has proven to be crucial in making such graph clustering and many of its downstream applications efficient in practice. While local clustering algorithms are already faster than traditional algorithms that touch the entire graph, they are sequential and there is an opportunity to make them even more efficient via parallelization. In this paper, we show how to parallelize many of these algorithms in the shared-memory multicore setting, and we analyze the parallel complexity of these algorithms. We present comprehensive experiments on large-scale graphs showing that our parallel algorithms achieve good parallel speedups on a modern multicore machine, thus significantly speeding up the analysis of local graph clusters in the very large-scale setting.


Shun is supported by the Miller Institute for Basic Research in Science at UC Berkeley. Roosta-Khorasani, Fountoulakis, and Mahoney are supported by the DARPA XDATA and GRAPHS programs. We thank the Intel Labs Academic Research Office for the Parallel Algorithms for Non-Numeric Computing Program for providing the machine for our experiments. We thank Guy Blelloch for early discussions on efficiently computing the conductance of sets in parallel.

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