Research map / Adversarial robustness and security / Attacks on text and graphs
Minimizing Trust Leaks for Robust Sybil Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn edge weights with negative labels (TSR) instead of SybilRank's uniform-degree random walk | Sybil detection on synthetic graphs · Random and adversarial attacks, synthetic | ROC-AUC | Helped |
| Learn edge weights with negative labels (TSR) instead of SybilRank | Sybil detection on Facebook graph · Adversarial attacks, Facebook | average AUROC | Helped |
| Use WMW loss in TSR instead of hinge loss (ablation) | TSR on Facebook random attacks · Random attacks, Facebook | average AUROC | Mixed |
| Use TSR with perfect victim detection vs. almost perfect for Integro | Integro on Facebook random attacks · Random attacks, Facebook | average AUROC | Mixed |
| Derive refined SybilRank guarantee under power-law honest graph assumption | SybilRank theory · Theoretical | asymptotic Sybil ranking bound O(g log n) | No clear effect |
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