An Efficient Subgraph Compression-Based Technique for Reducing the I/O Cost of Join-Based Graph Mining Algorithms

Abstract

Many join-based graph mining algorithms such as triangle listing and clique enumeration output a large size of intermediate or final data that sometimes dominates the mining cost. A few researches highlighted on the size of output data. However, those techniques have limitation that they are highly specific to their corresponding graph mining algorithms. In this paper, through the careful observations of the output patterns, we propose a general compression solution that can be applied to any join-based graph algorithm. It first categorizes the overlapping and non-overlapping vertices in a resultant subgraph set of a join-based graph mining algorithm. Then it compresses the output data by removing the redundancy from the overlapping vertices and by encoding the non-overlapping vertices using a non-aligned hybrid bit vector compression technique. Our proposed technique performs the compression on-the-fly and can easily be adopted by the join-based graph mining algorithms. Experiments on the real datasets show that our proposed technique, which is adopted in a triangle listing algorithm, reduces the size of the output data and the running time by three times and more than two times, respectively. The proposed technique also reduces the I/O cost for a maximal clique listing algorithm.