GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search

TL;DR

This paper introduces GTRACE-RS, an improved algorithm for mining frequent graph sequence patterns that significantly reduces computation time by utilizing reverse search, making it scalable for large and long sequences.

Contribution

It presents a novel reverse search-based approach to enhance the efficiency and scalability of graph sequence pattern mining compared to previous methods.

Findings

GTRACE-RS is several orders of magnitude faster than GTRACE.

The method is scalable for large graphs and long sequences.

Experimental results demonstrate improved efficiency and scalability.

Abstract

The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences. A method, called GTRACE, has been proposed to mine frequent patterns from graph sequences under the assumption that changes in graphs are gradual. Although GTRACE mines the frequent patterns efficiently, it still needs substantial computation time to mine the patterns from graph sequences containing large graphs and long sequences. In this paper, we propose a new version of GTRACE that enables efficient mining of frequent patterns based on the principle of a reverse search. The underlying concept of the reverse search is a general scheme for designing efficient algorithms for hard enumeration problems. Our performance study shows that the proposed method is efficient and scalable for mining both long and large graph sequence patterns and is several orders of magnitude faster than the original GTRACE.