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Finding frequent subgraph patterns in a big graph is an important problem with many applications such as classifying chemical compounds and building indexes to speed up graph queries. Since this problem is NP-hard, some recent parallel systems have been developed to accelerate the mining. However, they often have a huge memory cost, very long running time, suboptimal load balancing, and possibly inaccurate results. In this paper, we propose an efficient system called T-FSM for parallel mining of frequent subgraph patterns in a big graph. T-FSM adopts a novel task-based execution engine design to ensure high concurrency, bounded memory consumption, and effective load balancing. It also supports a new anti-monotonic frequentness measure called Fraction-Score, which is more accurate than the widely used MNI measure. Our experiments show that T-FSM is orders of magnitude faster than SOTA systems for frequent subgraph pattern mining. Our system code has been released at https://github.com/lyuheng/T-FSM.
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FSM-Explorer: An Interactive Tool for Frequent Subgraph Pattern Mining from a Big Graph
In this demonstration paper, we describe FSM-Explorer, an interactive tool for that makes it easier for end-users to mine frequent subgraph patterns from a big graph G, and to explore the subgraph instances in G that match the patterns. FSM-Explorer not only supports the popular MNI support measure, but also the recently proposed Fraction-Score measure that is more accurate. Its backend engine is built on top of the recent T-FSM system that ensures high concurrency, bounded memory consumption, and effective load balancing. Using real-world data, we showcase how users can mine frequent subgraph patterns by parameter tuning in FSM-Explorer, and how they can conveniently examine the many matched instances in G one batch at a time to improve productivity.
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- Award ID(s):
- 2229394
- PAR ID:
- 10498837
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 40th IEEE International Conference on Data Engineering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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