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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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,963 | Beyond Equi-joins: Ranking, Enumeration and Factorization | 2021 | VLDB | 5.2485815e-05 |
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 252 | Snorkel: Rapid Training Data Creation with Weak Supervision | 2018 | VLDB | 0.00030532082 |
| 1,724 | A General Framework for Estimating Graphlet Statistics via Random Walk | 2017 | VLDB | 0.00010736699 |
| 3,934 | ZooBP: Belief Propagation for Heterogeneous Networks | 2017 | VLDB | 6.6164175e-05 |
| 6,019 | Linearized and Single-Pass Belief Propagation | 2015 | VLDB | 5.2365238e-05 |
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