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BLITZ: A Flexible and Efficient Graph Mining System
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AI
DOI：10.1109/TKDE.2025.3624574.png)
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Abstract
En 中文
Graph pattern mining (GPM) is essential for uncovering complex patterns and relationships in graph data, with applications spanning social network analysis, bioinformatics, and recommendation systems. However, existing GPM systems face significant challenges, including high computational costs, limited scalability, and inefficiencies in handling large datasets. These systems can be categorized into two paradigms: embedding-centric systems, which struggle with the exponential growth of the search space, and pattern-centric systems, which often fail to leverage the full potential of input patterns. Despite their individual strengths, a critical research gap exists in understanding the comparative limitations of these approaches and the specific bottlenecks that hinder their performance. To address these limitations, we propose the gDAG model, a novel framework that unifies the computational processes of both paradigms, enabling comprehensive performance analysis. The gDAG model serves as the foundation for our BLITZ system, which incorporates innovative optimization techniques, such as path merging and quick counting. Our experimental results demonstrate that BLITZ achieves an average speedup of 10x in mining time compared to existing methods, significantly reducing execution time. Our experimental results demonstrate that BLITZ not only improves execution time but also provides a robust framework for future research.
Keywords:
Graph mining
subgraph matching
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