Multiple Source Detection without Knowing the Underlying Propagation Model

No. 1: Thirty-First AAAI Conference On Artificial Intelligence

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 31

AAAI Technical Track: AI and the Web

Information source detection, which is the reverse problem of information diffusion, has attracted considerable research effort recently. Most existing approaches assume that the underlying propagation model is fixed and given as input, which may limit their application range. In this paper, we study the multiple source detection problem when the underlying propagation model is unknown. Our basic idea is source prominence, namely the nodes surrounded by larger proportions of infected nodes are more likely to be infection sources. As such, we propose a multiple source detection method called Label Propagation based Source Identification (LPSI). Our method lets infection status iteratively propagate in the network as labels, and finally uses local peaks of the label propagation result as source nodes. In addition, both the convergent and iterative versions of LPSI are given. Extensive experiments are conducted on several real-world datasets to demonstrate the effectiveness of the proposed method.

10.1609/aaai.v31i1.10477