arXiv 30 Aug 2024 · Statistics — Methodology
arXiv:2408.17426 · PDF · DOI · OpenAlex · Extracted main text
In many online domains, Sybil networks -- or cases where a single user assumes multiple identities -- is a pervasive feature. This complicates experiments, as off-the-shelf regression estimators at least assume known network topologies (if not fully independent observations) when Sybil network topologies in practice are often unknown. The literature has exclusively focused on techniques to detect Sybil networks, leading many experimenters to subsequently exclude suspected networks entirely before estimating treatment effects. I present a more efficient solution in the presence of these suspected Sybil networks: a weighted regression framework that applies weights based on the probabilities that sets of observations are controlled by single actors. I show in the paper that the MSE-minimizing solution is to set the weight matrix equal to the inverse of the expected network topology. I demonstrate the methodology on simulated data, and then I apply the technique to a competition with suspected Sybil networks run on the Sui blockchain and show reductions in the standard error of the estimate by 6 - 24%.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Labs, Mysten (2023) Achievement Unlocked: Wrapping Up Quest 3 | 0.511 | 2 | 1 | 100% |
| 2 | Gong, Neil Zhenqiang, Frank, Mario, Mittal, Prateek (2014) SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection | 0.405 | 1 | 1 | 100% |
| 3 | Van Aken, Andrew (2024) Announcing Sybil Detection | 0.405 | 1 | 1 | 100% |
| 4 | Carroll, Raymond J, Ruppert, David (2017) Transformation and weighting in regression | 0.405 | 1 | 1 | 100% |
| 5 | Danezis, George, Mittal, Prateek (2009) Sybilinfer: Detecting sybil nodes using social networks. | 0.405 | 1 | 1 | 100% |
| 6 | Douceur, John R (2002) The Sybil Attack | 0.405 | 1 | 1 | 100% |
| 7 | Abadie, Alberto, Athey, Susan, Imbens, Guido W, Wooldridge, Jeffrey M (2022) When Should You Adjust Standard Errors for Clustering?* | 0.405 | 1 | 1 | 100% |
| 8 | Yang, Zhi, Wilson, Christo, Wang, Xiao, Gao, Tingting, Zhao, Ben Y.,… (2011) Uncovering Social Network Sybils in the Wild | 0.405 | 1 | 1 | 100% |
Showing the top 8 of 8 scored citations.