Efficient Partition-based Approaches for Diversified Top-k Subgraph Matching

TL;DR

This paper introduces a novel approach for diversified top-k subgraph matching that emphasizes topological diversity and efficiency, significantly outperforming existing methods in speed and coverage on real datasets.

Contribution

It proposes the DTkSM problem and the PDD framework, including two optimizations, to improve diversity and efficiency in subgraph matching.

Findings

Achieves up to 10,000x speedup over baselines

Reaches 80% of optimal distance diversity in results

Ensures 100% coverage diversity in top-k matches

Abstract

Subgraph matching is a core task in graph analytics, widely used in domains such as biology, finance, and social networks. Existing top-k diversified methods typically focus on maximizing vertex coverage, but often return results in the same region, limiting topological diversity. We propose the Distance-Diversified Top-k Subgraph Matching (DTkSM) problem, which selects k isomorphic matches with maximal pairwise topological distances to better capture global graph structure. To address its computational challenges, we introduce the Partition-based Distance Diversity (PDD) framework, which partitions the graph and retrieves diverse matches from distant regions. To enhance efficiency, we develop two optimizations: embedding-driven partition filtering and densest-based partition selection over a Partition Adjacency Graph. Experiments on 12 real world datasets show our approach achieves up to four orders of magnitude speedup over baselines, with 95% of results reaching 80% of optimal distance diversity and 100% coverage diversity.