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Online social networks (OSNs) are threatened by Sybil attacks, which createfake accounts (also called Sybils) on OSNs and use them for various maliciousactivities. Therefore, Sybil detection is a fundamental task for OSN security.Most existing Sybil detection methods are based on the graph structure of OSNs,and various methods have been proposed recently. However, although almost allmethods have been compared experimentally in terms of detection performance andnoise robustness, theoretical understanding of them is still lacking. In thisstudy, we show that existing graph-based Sybil detection methods can beinterpreted in a unified framework of low-pass filtering. This frameworkenables us to theoretically compare and analyze each method from twoperspectives: filter kernel properties and the spectrum of shift matrices. Ouranalysis reveals that the detection performance of each method depends on howwell low-pass filtering can extract low frequency components and remove noisyhigh frequency components. Furthermore, on the basis of the analysis, wepropose a novel Sybil detection method called SybilHeat. Numerical experimentson synthetic graphs and real social networks demonstrate that SybilHeatperforms consistently well on graphs with various structural properties. Thisstudy lays a theoretical foundation for graph-based Sybil detection and leadsto a better understanding of Sybil detection methods.

Interpreting Graph-based Sybil Detection Methods as Low-Pass Filtering