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

2025-11-24

AI Summary

Existing top-k diversified subgraph matching methods prioritize vertex coverage but often yield locally clustered results with insufficient topological diversity. This paper formalizes the Distance-Diversified Top-k Subgraph Matching (DTkSM) problem: selecting k pairwise isomorphic matches that maximize topological distance to enhance global structural coverage. To solve it, we propose a partitioned adjacency graph modeling framework—the first to incorporate pairwise topological distance as a core diversification criterion. Our approach integrates embedding-driven partition filtering, densest-subgraph-first candidate selection, and distance-diversity optimization, accelerated by efficient subgraph isomorphism checking and partition indexing. Evaluated on 12 real-world datasets, our method achieves up to four orders-of-magnitude speedup over state-of-the-art baselines; 95% of its outputs attain at least 80% of the optimal distance diversity, significantly improving both topological and coverage diversity.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Maximizes topological distance between subgraph matches for diversity
• Addresses computational complexity through graph partitioning techniques
• Improves efficiency while maintaining high coverage and distance diversity

Innovation

• Partition-based framework for diverse subgraph matching
• Embedding-driven partition filtering to enhance efficiency
• Densest-based selection over Partition Adjacency Graph