Effective Clustering for Large Multi-Relational Graphs
Summary: DEMM and DEMM+: a two-stage MRGC method that (i) learns node features by optimizing multi-relational Dirichlet energy and (ii) clusters by minimizing Dirichlet energy on the affinity graph. DEMM+ adds an efficient solver and a theory-backed transform to enable linear-time clustering without forming the N×N affinity, achieving high-quality, scalable clustering on million-node MRGs. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Xiaoyang Lin (Hong Kong Baptist University)
- 2. Runhao Jiang (Hong Kong Baptist University)
- 3. Renchi Yang (Hong Kong Baptist University)
BibTeX Citation
@inproceedings{lin_sigmod26,
title = {{Effective Clustering for Large Multi-Relational Graphs}},
author = {Lin, Xiaoyang and Jiang, Runhao and Yang, Renchi},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769784},
url = {https://dl.acm.org/doi/10.1145/3769784},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,188 | Efficient and Effective Attributed Hypergraph Clustering via K-Nearest Neighbor Augmentation | 2023 | SIGMOD | 5.4703528e-05 |
| 11,163 | Efficient High-Quality Clustering for Large Bipartite Graphs | 2024 | SIGMOD | 5.093636e-05 |
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