Efficient Discovery of Motif Transition Process for Large-Scale Temporal Graphs

Understanding the dynamic transition of motifs in temporal graphs is essential for revealing how graph structures evolve over time, identifying critical patterns, and predicting future behaviors, yet existing methods often focus on predefined motifs, limiting their ability to comprehensively capture transitions and interrelationships. We propose a parallel motif transition process discovery algorithm, PAMT, a novel parallel method for discovering motif transition processes in large-scale temporal graphs. PAMT integrates a parallel framework with the temporal zone partitioning strategy, which partitions temporal graphs by time and structure while preserving lossless motif transitions and enabling massive parallelism. PAMT comprises three phases: growth-zone parallel expansion, overlap-aware result aggregation, and deterministic encoding of motif transitions, ensuring accurate tracking of dynamic transitions and interactions. Results on nine large-scale real-world datasets demonstrate that PAMT achieves speedups ranging from 12.0 \(\times\) to 50.3 \(\times\) compared with the state-of-the-art method. The source code of our method is available at https://github.com/zzy0311/PAMT .