Efficient Mining of Frequent Subgraphs with Two-Vertex Exploration

TL;DR

This paper introduces a two-vertex exploration method for frequent subgraph mining that significantly improves speed and memory efficiency, enabling the mining of larger patterns than previous systems.

Contribution

It proposes a novel two-vertex exploration strategy combined with indexing and sampling techniques to enhance FSM performance and scalability.

Findings

Achieves significant speedups over state-of-the-art systems

Supports larger pattern mining tasks than existing methods

Reduces memory consumption and access overhead

Abstract

Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications. Numerous systems have been proposed for FSM in the past decade. Although these systems show good performance for small patterns (with no more than four vertices), we found that they have difficulty in mining larger patterns. In this work, we propose a novel two-vertex exploration strategy to accelerate the mining process. Compared with the single-vertex exploration adopted by previous systems, our two-vertex exploration avoids the large memory consumption issue and significantly reduces the memory access overhead. We further enhance the performance through an index-based quick pattern technique that reduces the overhead of isomorphism checks, and a subgraph sampling technique that mitigates the issue of subgraph explosion. The experimental results show that our system achieves significant speedups against the state-of-the-art graph pattern mining systems and supports larger pattern mining tasks that none of the existing systems can handle.