Wavefront-Based Multiple Rumor Sources Identification by Multi-Task Learning

Abstract

Identifying rumor sources in social networks is one of the key tasks for defeating rumors automatically. Many efforts have been devoted to locating rumor sources with an assumption that the infected status of each node is known in advance, while other efforts focus on identifying sources with partial infection knowledge, such as wavefront, sparse observers, and snapshots. Wavefront is a set of nodes that are infected at the latest propagation in social networks, which is originally defined for analyzing the SARS epidemic, and shows considerable importance in information source locating task. However, only a few studies are proposed to solve the multiple rumor source detection (MRSD) problem by using wavefront. In this paper, we propose a sequence-to-sequence model, called Graph Constraint based Sequential Source Identification (GCSSI), which takes wavefront as input to solve the MRSD problem. By adopting encoder-decoder structure and graph constraint based multi-task learning, GCSSI estimates the reverse rumor dissemination at each time step and predicts sources in an end-to-end way. We conduct experiments on several real datasets and the experimental results show the superiority of our model compared with existing work.