Research map / Learning theory and algorithms / Prediction and decision losses
On Manipulating Weight Predictions in Signed Weighted Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use indirect rather than direct attacks | FGA goodness manipulation · Bitcoin OTC, Bitcoin Alpha, and RFA Net | absolute goodness change: direct attack varies between 0.2 and 1.2; indirect attack is lower than 0.05 | Hurt |
| Add multiple Sybil edges per iteration instead of one | Greedy indirect attack on FGA · Bitcoin OTC | average absolute goodness change: 0.081 | Helped |
| Add multiple Sybil edges per iteration instead of one | Greedy indirect attack on FGA · Bitcoin Alpha | average absolute goodness change: 0.085 | Helped |
| Add multiple Sybil edges per iteration instead of one | Greedy indirect attack on FGA · RFA Net | average absolute goodness change: 0.030 | Helped |
| Use established rather than not-established attackers | FGA direct and indirect attacks · Bitcoin OTC, Bitcoin Alpha, and RFA Net | goodness change | No clear effect |
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