From Appearances to Essence: Learning Diffusion Networks from Final Infection Results (UNDER REVIEW)

Published in IJCAI (UNDER REVIEW), 2019

Authors: Qian Yan, Hao Huang, Yunjia Zhang

Abstract: To infer the structures of diffusion networks, most of the existing approaches require not only the final infection statuses of the nodes in the network, but also the temporal information that pinpoints the exact time when infections occur. Nonetheless, in many natural diffusion processes such as the spread of epidemics, monitoring the exact infection time is usually difficult and expensive. In this paper, we investigate the problem of inferring the diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing the sequence of infection time to determine the potential parent-child influence relationships between the nodes, we present a conditional entropy-based method to infer the candidate parent nodes for each node in the network. Furthermore, to refine the inferred diffusion network structure, we disqualify the insignificant candidate parent nodes whose infections have very weak correlations with the infections of the corresponding child nodes. Experimental results on both synthetic and real-world networks verify the effectiveness and efficiency of our approach.