l2Match: Optimizing Subgraph Isomorphism for Efficient Small Query Matching on Labelled Graphs.

This research addresses Subgraph Isomorphism, proposing four optimization techniques within a filter-and-verification framework to reduce search space. Filtering maps query graph vertices to potential data graph candidates, pruning invalid options. Verification recursively explores solutions, with recent studies suggesting stronger constraints and heuristic approaches. A new algorithm, l2Match, integrates Label-Pair Indexing, Backward Candidate Pruning, and Jump-and-Redo methods for small queries, outperforming existing solutions. Real-world and synthetic dataset evaluations confirm l2Match's superiority, highlighting its advantages over Constraint Programming. However, label-based indexing efficacy varies with datasets. Applying optimizations to existing algorithms enhances query completion times.