Identifying Anomalous Edges with Link Sampling and Consensus

Abdullah Karaaslanli, Panagiotis A. Traganitis, Selin Aviyente

Abstract: Graphs and graph structured data are ubiquitous in many applications as they readily represent datasets that lie in irregular, yet structured, domains. Due to their popularity, a plethora of methods have been developed to learn from graphstructured data, which have been shown to be effective in many real-world applications including biology, finance, and social sciences, among others. However, these methods generally assume that the observed graph is free of corruption. This assumption does not hold in cases where the graph includes structural contamination, such as anomalous edges, which can degrade learning performance. This paper presents a method to identify anomalous edges that can be employed prior to learning methods to mitigate their effects. The proposed method employs link prediction (LP) to assign likelihood scores to the observed edges. As LP is not anomaly aware, we combine LP with ideas from sampling and consensus algorithms. LP is applied to subgraphs which tend to have fewer anomalies. Edge anomaly scores are then obtained by judiciously combining LP prediction results across subgraphs. Preliminary results indicate the effectiveness of the proposed method.