  Many approaches focus on detecting dense blocks in the tensor of multimodal
data to prevent fraudulent entities (e.g., accounts, links) from retweet
boosting, hashtag hijacking, link advertising, etc. However, no existing method
is effective to find the dense block if it only possesses high density on a
subset of all dimensions in tensors. In this paper, we novelly identify
dense-block detection with dense-subgraph mining, by modeling a tensor into a
weighted graph without any density information lost. Based on the weighted
graph, which we call information sharing graph (ISG), we propose an algorithm
for finding multiple densest subgraphs, D-Spot, that is faster (up to 11x
faster than the state-of-the-art algorithm) and can be computed in parallel. In
an N-dimensional tensor, the entity group found by the ISG+D-Spot is at least
1/2 of the optimum with respect to density, compared with the 1/N guarantee
ensured by competing methods. We use nine datasets to demonstrate that
ISG+D-Spot becomes new state-of-the-art dense-block detection method in terms
of accuracy specifically for fraud detection.
