Research map / Graph learning / Graph algorithms and clustering
How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use PR-Kron or PR-PD instead of the compared methods | Graph ranking · Synthetic graphs | pairwise 0-1 loss | Helped |
| Use PR-Kron or PR-PD instead of the compared methods | Graph ranking · Heart, Fourclass, Vehicle, Vowel, House, Mg | pairwise 0-1 loss | Helped |
| Use Rank Centrality or Inductive Pairwise Ranking instead of graph-locality-based methods | Graph ranking · Synthetic graphs | pairwise 0-1 loss | Hurt |
| Use Rank Centrality or Inductive Pairwise Ranking instead of graph-locality-based methods | Graph ranking · Real-world datasets | pairwise 0-1 loss | Hurt |
| Apply full ranking rather than bipartite ranking | Graph ranking · Real-world datasets | pairwise 0-1 loss | Hurt |
Closest papers by meaning
- Inductive Pairwise Ranking: Going Beyond the n log(n) Barrier (AAAI 2017 · similarity 0.88)
- Learning with Structure: Computing Consistent Subsets on Structurally-Regular Graphs (AAAI 2026 · similarity 0.87)
- Balanced Ranking with Relative Centrality: A multi-core periphery perspective (ICLR 2025 · similarity 0.87)
- Node Similarities under Random Projections: Limits and Pathological Cases (ICLR 2025 · similarity 0.87)
- Learning Graph Representation via Graph Entropy Maximization (ICML 2024 · similarity 0.85)
- No Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets (ICML 2025 · similarity 0.85)
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