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, PTMT, a novel parallel method for discovering
motif transition processes in large-scale temporal graphs. PTMT integrates a
tree-based framework with the temporal zone partitioning (TZP) strategy, which
partitions temporal graphs by time and structure while preserving lossless
motif transitions and enabling massive parallelism. PTMT comprises three
phases: growth zone parallel expansion, overlap-aware result aggregation, Und
deterministic encoding of motif transitions, ensuring accurate tracking of
dynamic transitions and interactions. Results on 10 real-world datasets
demonstrate that PTMT achieves speedups ranging from 12.0$\times$ to
50.3$\times$ compared to the SOTA method.

Dieser Artikel untersucht Zeitreisen und deren Auswirkungen.

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