Research map / Graph learning / Graph algorithms and clustering
Sublinear-Time Clustering Oracle for Signed Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use signed rather than unsigned random walks | rw-seeded · WikiS biclustering | accuracy | Helped |
| Ignore edge signs and use the underlying unsigned graph (ablation) | rw-u-seeded · WikiS biclustering | accuracy: above 50% but below 60% | Hurt |
| Provide ground-truth seed nodes during preprocessing (ablation) | random-walk oracle · WikiS clustering | accuracy | Helped |
| Use unseeded initialization instead of ground-truth seeds (ablation) | random-walk oracle · WikiS biclustering | accuracy | Hurt |
| Increase random-walk length (ablation) | unseeded oracle · WikiS clustering | accuracy | Helped |
| Increase the number of clusters, making clusters smaller | polarSeeds · Synthetic data | accuracy | Helped |
| Use the signed oracle for clustering (ablation) | signed and unsigned random-walk oracles · Synthetic data clustering | accuracy | No clear effect |
| Use the signed oracle to recover opposing biclusters (ablation) | signed and unsigned random-walk oracles · Synthetic data biclustering | accuracy | Helped |
| Use more random walks (ablation) | rw-seeded · WikiM biclustering | accuracy: over 83% | Helped |
| Use more random walks, up to 10,000 (ablation) | rw-seeded · WikiL biclustering | accuracy: 50% | Mixed |
| Apply the oracle to large communities that partition the graph | signed clustering oracle · Synthetic data | accuracy | Helped |
Closest papers by meaning
- An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks (NeurIPS 2025 · similarity 0.89)
- A Near-Linear Time Approximation Algorithm for Beyond-Worst-Case Graph Clustering (ICML 2024 · similarity 0.86)
- Optimal Graph Clustering without Edge Density Signals (NeurIPS 2025 · similarity 0.86)
- Optimal Quasi-clique: Hardness, Equivalence with Densest-k-Subgraph, and Quasi-partitioned Community Mining (AAAI 2024 · similarity 0.86)
- Online Sparsification of Bipartite-Like Clusters in Graphs (ICML 2025 · similarity 0.86)
- Dynamic Correlation Clustering in Sublinear Update Time (ICML 2024 · similarity 0.86)
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