TeMatch: A Fast Temporal Subgraph Matching Framework with Temporal-Aware Subgraph Matching Algorithms
Chengying Huan, Heng Zhang, Yongchao Liu, Likang Chen, Xuran Wang, Yongchun Jiang, Shaonan Ma, Yanjun Wu · 2025
Temporal subgraph matching aims to identify occurrences of a query graph within a large target graph, subject to certain temporal constraints that require that timestamps on the edges increase in accordance with the direction of the path. Current research on temporal subgraph matching typically identifies each non-temporal match and then filters out occurrences by examining all paths within each occurrence for compliance with temporal constraints. However, this approach proves to be highly inefficient, as it involves excessive unnecessary computation on non-temporal occurrences that do not meet the temporal constraints and can be pruned early during the matching process. Moreover, the constraint examination on all paths within each occurrence further results in numerous redundant timestamp comparisons. Therefore, a high-performance solution is demanded to overcome these drawbacks. In this paper, we introduce TeMatch, a high-performance framework designed to be compatible with any enumeration-based solution for temporal subgraph matching. TeMatch features a novel topological representation of temporal constraints in the query graph, along with three temporal-aware subgraph matching algorithms that enable rapid constraint checking and enhance early pruning and filtration. Extensive experiments reveal that TeMatch efficiently harnesses temporal information to enable early pruning and achieves a speedup of 313.57x while being parallel-friendly, highly compatible, and yielding identical matching results.