Mayura: Exploiting Similarities in Motifs for Temporal Co-Mining

2025-07-20

AI Summary

To address redundant computation arising from isolated mining of multiple temporal motifs in temporal graphs, this paper proposes Mayura, a unified joint mining framework. Its core innovation is the Motif-Group Tree (MG-Tree), a hierarchical structure that explicitly models structural and temporal commonalities among motifs, enabling cross-motif search-path reuse and computational sharing. We further design an MG-Tree–driven cooperative mining algorithm and implement an elastic runtime architecture compatible with both CPU and GPU, balancing efficiency and scalability. Evaluated on real-world datasets, Mayura achieves average speedups of 2.4× on CPU and 1.7× on GPU over single-motif baselines, while guaranteeing exact result correctness.

Technology Category

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

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: Topic discovery and tracking
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

Temporal graphs serve as a critical foundation for modeling evolving interactions in domains ranging from financial networks to social media. Mining temporal motifs is essential for applications such as fraud detection, cybersecurity, and dynamic network analysis. However, conventional motif mining approaches treat each query independently, incurring significant redundant computations when similar substructures exist across multiple motifs. In this paper, we propose Mayura, a novel framework that unifies the mining of multiple temporal motifs by exploiting their inherent structural and temporal commonalities. Central to our approach is the Motif-Group Tree (MG-Tree), a hierarchical data structure that organizes related motifs and enables the reuse of common search paths, thereby reducing redundant computation. We propose a co-mining algorithm that leverages the MG-Tree and develop a flexible runtime capable of exploiting both CPU and GPU architectures for scalable performance. Empirical evaluations on diverse real-world datasets demonstrate that Mayura achieves substantial improvements over the state-of-the-art techniques that mine each motif individually, with an average speed-up of 2.4x on the CPU and 1.7x on the GPU, while maintaining the exactness required for high-stakes applications.

Problem

• Reduces redundant computations in temporal motif mining
• Exploits structural and temporal similarities across motifs
• Enables scalable co-mining on CPU and GPU architectures

Innovation

• MG-Tree organizes motifs for path reuse
• Co-mining algorithm leverages MG-Tree structure
• Flexible runtime supports CPU and GPU architectures