Multi-Level Graph Representation Learning Through Predictive Community-based Partitioning
Summary: ML-GRL recursively partitions graphs, selecting the best community-detection algorithm for each subgraph and using global graphs to preserve overall topology. A pre-trained predictor estimates performance without partitioning, enabling parallel subgraph processing and yielding accuracy and speedups over six GRL baselines. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Bo-Young Lim (Seoul National University)
- 2. Jeong-Ha Park (Seoul National University)
- 3. Kisung Lee (Louisiana State University)
- 4. Hyuk-Yoon Kwon (Seoul National University)
BibTeX Citation
@inproceedings{lim_sigmod25,
title = {{Multi-Level Graph Representation Learning Through Predictive Community-based Partitioning}},
author = {Lim, Bo-Young and Park, Jeong-Ha and Lee, Kisung and Kwon, Hyuk-Yoon},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3711115},
url = {https://dl.acm.org/doi/10.1145/3711115},
year = {2025}
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