关键词: 传播网络推断, 影响关系, 感染传播概率, 感染时间信息

Abstract: Most existing approaches to diffusion network inference require the infection temporal information of nodes. But in many real-world diffusion processes, the exact infection temporal information is often unavailable. This paper aims at an effective, efficient and infection temporal information-free approach for diffusion network inference, and studies how to infer the influence relationships and transmission rates between the nodes based on only the final infection statuses of the nodes observed in a number of diffusion processes. To this end, the approach proposed in this paper first utilizes the mutual information of infection statuses among the nodes to quantify the correlation of the infections and reveal the underlying influence relationships between the nodes. Then, the proposed approach constructs the log-likelihood function of the observed infection statuses with transmission rates as the variables, and adopts the expectation-maximization algorithm to maximize the function and approximate the optimal transmission rates. Extensive experimental results demonstrate that compared against the existing approaches, the proposed approach has a reasonably better accuracy performance on diffusion network inference, and significantly reduces runtime.

Key words: diffusion network inference, influence relationships, transmission rates, infection temporal information