Research map / Graph learning / Graph algorithms and clustering
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the q-norm local cut objective and strongly local solver rather than ACL | SLQ vs. ACL · Facebook class year 2009 across seven school networks | F1: UCLA 0.9 vs 0.9; MIT 0.9 vs 0.8; Duke 1.0 vs 0.9 | Mixed |
| Use q-Huber loss instead of the power loss (ablation) | SLQ · Facebook class-year clusters | F1: 2009 UCLA 0.9 -> 0.9; 2009 MIT 0.9 -> 0.8; 2008 MIT 0.5 -> 0.5 | Mixed |
| Use the q-norm cut solver instead of CRD-3 | SLQ · Facebook class year 2009, UCLA | F1: 0.3 -> 0.9 | Helped |
| Use the q-norm cut solver instead of CRD-5 | SLQ · Facebook school networks, class year 2009 | running time (seconds): 123 vs 9378 | Helped |
| Increase seed-set size | SLQ and ACL · MIT Facebook class year 2008 | F1 | Helped |
| Use q>2 rather than PageRank (q=2) | SLQ · DBLP and LiveJournal recall experiment | recall | Helped |
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