  Fraud detection is extremely critical for e-commerce business. It is the
intent of the companies to detect and prevent fraud as early as possible.
Existing fraud detection methods try to identify unexpected dense subgraphs and
treat related nodes as suspicious. Spectral relaxation-based methods solve the
problem efficiently but hurt the performance due to the relaxed constraints.
Besides, many methods cannot be accelerated with parallel computation or
control the number of returned suspicious nodes because they provide a set of
subgraphs with diverse node sizes. These drawbacks affect the real-world
applications of existing methods. In this paper, we propose an Ensemble-based
Fraud Detection (EnsemFDet) method to scale up fraud detection in bipartite
graphs by decomposing the original problem into subproblems on small-sized
subgraphs. By oversampling the graph and solving the subproblems, the ensemble
approach further votes suspicious nodes without sacrificing the prediction
accuracy. Extensive experiments have been done on real transaction data from
JD.com, which is one of the world's largest e-commerce platforms. Experimental
results demonstrate the effectiveness, practicability, and scalability of
EnsemFDet. More specifically, EnsemFDet is up to 100x faster than the
state-of-the-art methods due to its parallelism with all aspects of data.
