Collaborative Inference of Coexisting Information Diffusions

The purpose of diffusion history inference is to reconstruct the missing traces of information diffusion according to incomplete observations. Existing methods, however, often focus only on single diffusion trace, while in a real-world social network, there often coexist multiple information diffusions. In this paper, we propose a novel approach called Collaborative Inference Model (CIM) for the problem of the inference of coexisting information diffusions. CIM can holistically model multiple information diffusions without any prior assumption of diffusion models, and collaboratively infer the histories of the coexisting information diffusions via low-rank approximation with a fusion of heterogeneous constraints generated from additional data sources. We also propose an optimized algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA) to speed up the inference without compromise on the accuracy. Extensive experiments are conducted on real-world datasets to verify the effectiveness and efficiency of CIM and TWPDA.