Near-optimal Top-k Pattern Mining

TL;DR

This paper introduces a cost-effective, level-wise method for mining near-optimal top-k frequent patterns in large graphs, significantly improving efficiency and scalability over traditional methods.

Contribution

It presents a novel approach that efficiently finds top-k patterns by early termination and support lower bound computation, reducing computational costs.

Findings

Outperforms traditional methods in efficiency and memory usage

Achieves higher recall and scalability in experiments

Effective on both real-life and synthetic graphs

Abstract

Nowadays, frequent pattern mining (FPM) on large graphs receives increasing attention, since it is crucial to a variety of applications, e.g., social analysis. Informally, the FPM problem is defined as finding all the patterns in a large graph with frequency above a user-defined threshold. However, this problem is nontrivial due to the unaffordable computational and space costs in the mining process. In light of this, we propose a cost-effective approach to mining near-optimal top-k patterns. Our approach applies a "level-wise" strategy to incrementally detect frequent patterns, hence is able to terminate as soon as top-k patterns are discovered. Moreover, we develop a technique to compute the lower bound of support with smart traverse strategy and compact data structures. Extensive experimental studies on real-life and synthetic graphs show that our approach performs well, i.e., it outperforms traditional counterparts in efficiency, memory footprint, recall and scalability.