Efficient cohesive subgraphs detection in parallel

A cohesive subgraph is a primary vehicle for massive graph analysis, and a newly introduced cohesive subgraph, κ-truss, which is motivated by a natural observation of social cohesion, has attracted more and more attention. However, the existing parallel solutions to identify the k-truss are inefficient for very large graphs, as they still suffer from huge communication cost and large number of iterations during the computation. In this paper, we propose a novel parallel and efficient truss detection algorithm, called PETA. The PETA produces a triangle complete subgraph (TC-subgraph) for every computing node. Based on the TC-subgraphs, PETA can detect the local k-truss in parallel within a few iterations. We theoretically prove, within this new paradigm, the communication cost of PETA is bounded by three times of the number of triangles, the total computation complexity of PETA is the same order as the best known serial algorithm and the number of iterations for a given partition scheme is minimized as well. Furthermore, we present a subgraph-oriented model to efficiently express PETA in parallel graph computing systems. The results of comprehensive experiments demonstrate, compared with the existing solutions, PETA saves 2X to 19X in communication cost, reduces 80% to 95% number of iterations and improves the overall performance by 80% across various real-world graphs