T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph
Summary: T-FSM's task-based parallel engine delivers high concurrency with bounded memory and balanced load for large-scale subgraph mining. It introduces Fraction-Score, a more accurate anti-monotonic measure than MNI, with speedups and open-source release. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lyuheng Yuan (University of Alabama)
- 2. Da Yan (University of Alabama)
- 3. Wenwen Qu (East China Normal University)
- 4. Saugat Adhikari (University of Alabama)
- 5. Jalal Khalil (University of Alabama)
- 6. Cheng Long (Nanyang Technological University)
- 7. Xiaoling Wang (East China Normal University)
BibTeX Citation
@inproceedings{yuan_sigmod23,
title = {{T-FSM: A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph}},
author = {Yuan, Lyuheng and Yan, Da and Qu, Wenwen and Adhikari, Saugat and Khalil, Jalal and Long, Cheng and Wang, Xiaoling},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588928},
url = {https://dl.acm.org/doi/10.1145/3588928},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,601 | Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods | 2025 | VLDB | 5.5866563e-05 |
| 10,280 | Periodic Community Search in Temporal Graphs: Time Series-based Methods | 2026 | SIGMOD | 5.093636e-05 |
| 10,748 | Accelerating Skyline Path Enumeration with a Core Attribute Index on Multi-attribute Graphs | 2025 | SIGMOD | 5.093636e-05 |
| 10,898 | Maximum k-Plex Finding: Choices of Pruning Techniques Matter! | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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