Factorized Graph Representations for Semi-Supervised Learning from Sparse Data
Summary: Factorized graph representations enable distant compatibility estimation for semi-supervised learning on ultra-sparse graphs. Using size-independent graph sketches and algebraic amplification, the estimator runs orders of magnitude faster and achieves accuracy comparable to gold compatibilities, providing a cheap pre-processing step for label propagation. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Krishna Kumar P. (Indian Institute of Technology Madras)
- 2. Paul Langton (Northeastern University)
- 3. Wolfgang Gatterbauer (Northeastern University)
BibTeX Citation
@inproceedings{p_sigmod20,
title = {{Factorized Graph Representations for Semi-Supervised Learning from Sparse Data}},
author = {P., Krishna Kumar and Langton, Paul and Gatterbauer, Wolfgang},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380577},
url = {https://dl.acm.org/doi/10.1145/3318464.3380577},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,506 | Beyond Equi-joins: Ranking, Enumeration and Factorization | 2021 | VLDB | 6.0883761e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 200 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00025443474 |
| 1,944 | A General Framework for Estimating Graphlet Statistics via Random Walk | 2017 | VLDB | 9.3078746e-05 |
| 5,110 | ZooBP: Belief Propagation for Heterogeneous Networks | 2017 | VLDB | 6.257964e-05 |
| 7,135 | Linearized and Single-Pass Belief Propagation | 2015 | VLDB | 5.580601e-05 |
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