Multi-task Inference of Diffusion Networks

Inferring the underlying structures of diffusion networks based on observed diffusion results is a fundamental problem in network analysis. Traditional approaches typically address this problem by inferring each diffusion network in isolation, relying on the assumption that sufficient observation data is available for each individual inference task. However, in many real-world scenarios, it is common to observe diffusion processes occur across multiple networks with similar structures, while the amount of observable data collected on each network is often limited. In this work, we study how to infer multiple similar diffusion networks jointly with limited observation data for each network. To this end, we propose a novel iterative strategy which in turn updates the inference results for all diffusion networks by exploiting the similarity between the networks, and theoretically guarantee the monotonicity and convergence of the iterative process. Extensive experiments on both synthetic and real-world networks demonstrate that our method not only achieves superior inference accuracy compared to existing techniques, but also maintains high computational efficiency.