Dynamic Subgraph Matching via Cost-Model-based Vertex Dominance Embeddings (Technical Report)

Abstract

In many real-world applications such as social network analysis, knowledge graph discovery, biological network analytics, and so on, graph data management has become increasingly important and has drawn much attention from the database community. While many graphs (e.g., Twitter, Wikipedia, etc.) are usually involving over time, it is of great importance to study the dynamic subgraph matching (DSM) problem, a fundamental yet challenging graph operator, which continuously monitors subgraph matching results over dynamic graphs with a stream of edge updates. To efficiently tackle the DSM problem, we carefully design a novel vertex dominance embedding approach, which effectively encodes vertex labels that can be incrementally maintained upon graph updates. Inspire by low pruning power for high-degree vertices, we propose a new degree grouping technique over basic subgraph patterns in different degree groups (i.e., groups of star substructures), and devise degree-aware star substructure synopses (DAS^3) to effectively facilitate our designed vertex dominance and range pruning strategies. We develop efficient algorithms to incrementally maintain dynamic graphs and answer DSM queries. Through extensive experiments, we confirm the efficiency of our proposed approaches over both real and synthetic graphs.