Fraud Detection using Graph Topology and Temporal Spikes
Fraud detection is facing more challenges when online fraudsters invest more resources, including purchasing large pools of fake user accounts and dedicated IPs, to make their attacks less obvious. Existing approaches such as average degree maximization in adjacency matrices or tensors, that aimed at finding the connections of maximum average degree density suffer from the bias of including more false positive nodes, resulting in lower accuracy and increased need for manual verification. Therefore, we propose HoloScope method to detect topology and spike suspiciousness simultaneously. A novel "contrast suspiciousness" is introduced for honoring contrast behaviors between fraudsters and normal users. In terms of graph topology, it allows us to more accurately detect fraudulent blocks, reducing the false positive nodes; In terms of temporal spikes, HoloScope takes into account the bursts caused by fraudsters' attacking, and the sudden drops due to the poor attractiveness of fake objects. In addition, we provide theoretical bounds for how much this increases the time cost needed for fraudsters to conduct adversarial attacks. Additionally, from the perspective of ratings, HoloScope incorporates the deviation of rating scores in order to catch fraudsters more accurately. Moreover, the HoloScope has a concise framework and sub quadratic time complexity, which make our algorithm reproducible and scalable. In the experiments, HoloScope achieved significant accuracy improvements on both synthetic and real data, compared with the state of the art baselines.