PECC: parallel expansion based on clustering coefficient for efficient graph partitioning

Abstract

In the pursuit of graph processing performance, graph partitioning, as a crucial preprocessing step, has been widely concerned. Based on an in-depth analysis of Neighbor Expansion (NE) graph partitioning algorithm, we propose Parallel Expansion based on Clustering Coefficient (PECC). Firstly, to address the partition disturbance caused by internal structural changes during the process of vertex neighborhood expansion in the traditional NE algorithm, we perform a formal redefinition of the vertex state during the partitioning process and introduce the concept of clustering coefficient. Then, PECC uses the clustering coefficient as a metric to measure the closeness between vertices and potential partitions. Based on this metric, a novel parallel partitioning strategy in the distributed environment is proposed. This strategy consists of two core steps: the expansion process and the allocation process. Through two steps, PECC can effectively improve the operating efficiency of programs and significantly reduce the partitioning time. In addition, to ensure data consistency during parallel expansion, we adopt a distributed locking engine to solve concurrency management problems. Our evaluations on large real-world graphs show that in many cases, PECC achieves a balance between partitioning quality and computational efficiency. Finally, we show that PECC integrated on GraphX outperforms the built-in native algorithms.