Abstract
Subgraph isomorphism algorithms face significant scalability bottlenecks on large-scale property graphs due to inefficient vertex-by-vertex search that requires extensive exploration of early search tree levels where pruning is minimal. We present HiPerMotif, a hybrid parallel algorithm that overcomes these limitations through edge-centric initialization. HiPerMotif first reorders pattern graphs to prioritize high-connectivity vertices, then systematically identifies and validates all possible first-edge mappings before injecting these pre-validated partial states directly at search depth 2. This approach eliminates costly early exploration while enabling natural parallelization over independent edge candidates. Comprehensive evaluation against state-of-the-art baselines (VF2-PS, VF3P, Glasgow) demonstrates up to 66x speedup on real-world networks and successful processing of massive datasets like the 150M-edge H01 human connectome that cause existing methods to fail due to memory constraints. Implemented in the open-source Arkouda/Arachne framework, HiPerMotif enables previously intractable large-scale network analysis in computational neuroscience and related domains.
Mohammad Dindoost
PhD Student
Oliver Alvarado Rodriguez
Research Software Engineer
David A. Bader
Distinguished Professor and Director of the Institute for Data Science
David A. Bader is a Distinguished Professor in the Department of Computer Science at New Jersey Institute of Technology.