Scalable Continuous-time Diffusion Framework for Network Inference and
  Influence Estimation
Keke Huang Ruize Gao Bogdan Cautis Xiaokui Xiao
The study of continuous-time information diffusion has been an important area
of research for many applications in recent years. When only the diffusion
traces (cascades) are accessible, cascade-based network inference and influence
estimation are two essential problems to explore. Alas, existing methods
exhibit limited capability to infer and process networks with more than a few
thousand nodes, suffering from scalability issues. In this paper, we view the
diffusion process as a continuous-time dynamical system, based on which we
establish a continuous-time diffusion model. Subsequently, we instantiate the
model to a scalable and effective framework (FIM) to approximate the diffusion
propagation from available cascades, thereby inferring the underlying network
structure. Furthermore, we undertake an analysis of the approximation error of
FIM for network inference. To achieve the desired scalability for influence
estimation, we devise an advanced sampling technique and significantly boost
the efficiency. We also quantify the effect of the approximation error on
influence estimation theoretically. Experimental results showcase the
effectiveness and superior scalability of FIM on network inference and
influence estimation.