  As online fraudsters invest more resources, including purchasing large pools
of fake user accounts and dedicated IPs, fraudulent attacks become less obvious
and their detection becomes increasingly challenging. Existing approaches such
as average degree maximization suffer from the bias of including more nodes
than necessary, resulting in lower accuracy and increased need for manual
verification. Hence, we propose HoloScope, which uses information from graph
topology and temporal spikes to more accurately detect groups of fraudulent
users. In terms of graph topology, we introduce "contrast suspiciousness," a
dynamic weighting approach, which allows us to more accurately detect
fraudulent blocks, particularly low-density blocks. In terms of temporal
spikes, HoloScope takes into account the sudden bursts and drops of fraudsters'
attacking patterns. 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, HoloScope has a concise framework and sub-quadratic time complexity,
making the algorithm reproducible and scalable. Extensive experiments showed
that HoloScope achieved significant accuracy improvements on synthetic and real
data, compared with state-of-the-art fraud detection methods.
