S3AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics (Technical Report)
Abstract
For the past decades, the \textit{subgraph similarity search} over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely \textit{subgraph similarity search under aggregated neighbor difference semantics} (SAND), which identifies subgraphs in a data graph that are similar to a given query graph by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the SAND problem, we design two effective pruning methods, \textit{keyword set} and \textit{aggregated neighbor difference lower bound pruning}, which rule out false alarms of candidate vertices/subgraphs to reduce the SAND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient SAND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our SAND approach over both real and synthetic graphs under various parameter settings.
Cite
@article{arxiv.2505.00393,
title = {S3AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics (Technical Report)},
author = {Qi Wen and Yutong Ye and Xiang Lian and Mingsong Chen},
journal= {arXiv preprint arXiv:2505.00393},
year = {2025}
}