SparRL: Graph Sparsification via Deep Reinforcement Learning
Summary: SparRL: a general RL-based framework for graph sparsification, with size-independent cost and flexible reduction objectives. Empirical results show SparRL surpasses baselines on diverse objectives, underscoring broad applicability to graph representation tasks. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ryan Wickman (University of Memphis)
BibTeX Citation
@inproceedings{wickman_sigmod22,
title = {{SparRL: Graph Sparsification via Deep Reinforcement Learning}},
author = {Wickman, Ryan},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520254},
url = {https://dl.acm.org/doi/10.1145/3514221.3520254},
year = {2022}
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|---|---|---|---|---|
| 1,163 | Local Graph Sparsification for Scalable Clustering | 2011 | SIGMOD | 0.00011865557 |
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