S³AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics

For the past decades, the subgraph similarity search over a largescale 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 subgraph similarity search under aggregated neighbor difference semantics (³AND), which identifies subgraphs g in a data graph G that are similar to a given query graph q by considering both keywords and graph structures (under new keyword/structural matching semantics). To efficiently tackle the ³AND problem, we design two effective pruning methods, keyword set and aggregated neighbor difference lower bound pruning, which rule out false alarms of candidate vertices/subgraphs to reduce the ³AND search space. Furthermore, we construct an effective indexing mechanism to facilitate our proposed efficient ³AND query answering algorithm. Through extensive experiments, we demonstrate the effectiveness and efficiency of our S3AND approach over both real and synthetic graphs under various parameter settings.