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

2025-04-22

AI Summary

Existing motif transition mining methods for large-scale temporal graphs are constrained by predefined motifs and fail to capture dynamic evolutionary patterns. Method: We propose the first scalable framework capable of automatically discovering motif transition chains. Our approach innovatively integrates a tree-based index with lossless time-zone partitioning (TZP), enabling a parallel graph traversal paradigm that supports dynamic growth-region expansion, overlap-aware aggregation, and deterministic transition encoding. Contributions/Results: (1) It eliminates the need for motif predefinition, enabling end-to-end discovery of evolutionary paths; (2) it processes temporal graphs with up to one billion edges in milliseconds; and (3) on ten real-world datasets, it achieves 12.0×–50.3× speedup over state-of-the-art methods while significantly improving both accuracy and scalability.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Temporal Planning
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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, and 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$ imes$ to 50.3$ imes$ compared to the SOTA method.

Problem

• Discovering dynamic motif transitions in large temporal graphs
• Overcoming limitations of predefined motifs in existing methods
• Enabling parallel processing for efficient motif transition analysis

Innovation

• Parallel motif transition discovery algorithm
• Tree-based framework with TZP strategy
• Three-phase accurate transition tracking