Parallel and Distributed Triangle Counting on Graph Streams

This paper studies the problem of approximating the number of triangles in a graph whose edges arrive as a stream. The number of triangles in a graph is an important metric in social network analysis, link classification and recommendation, and more. We present efficient algorithms for both shared-memory parallel (multicore-like) processing of a single stream where edges arrive in batches and processing of streams by multiple physically distributed processors. For the shared-memory setting, we give the first parallel cache-oblivious algorithm with good theoretical guarantees for accuracy; we also experimentally verify that the algorithm obtains substantial speedups and accurate estimates. For the distributed setting, we present a distributed algorithm with low message complexity, improving upon existing sketch-based algorithms in common settings.

By: A. Pavan, Kanat Tangwongan, Srikanta Tirthapura

Published in: RC25352 in 2013


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