Vertex-Cut Partitioning Performance Analysis for FASTCD Algorithm in Large-Scale Graph

Abstract

Large-scale graph processing has become a popular research topic in domain graph partitioning and community detection. FastCD is a community-based detection algorithm based on modularity optimization capable of detecting communities on large-scale graphs. Community detection on large graphs requires graph partitioning techniques that partition large-scale graphs into several subgraphs for processing carried out in parallel, so that computational loads can be distributed across machines in the cluster. The research was conducted on graph-parallel distributed framework, GraphX, which is a graph processing component in Spark. The strategies of vertex-cut partitioning method such as RandomVertexCut, CanonicalRandomVertexCut, EdgePartition1D, and EdgePartition2D applied to the FastCD to perform community detection on large scale graphs in parallel. Furthermore, EdgePartition1D has the best performance for FastCD performing parallel community detection on large-scale graphs with the number of edges 7,600,595 and vertices 685,230. The results of this research can be seen that the FastCD algorithm with EdgePartition1D and EdgePartition2D is able to maintain valuable information contained in graphs by considering vertices and neighboring edges quickly.