Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation
This addresses scalability issues in network analysis for applications like social media or epidemiology, though it appears incremental as it builds on existing cascade-based methods.
The authors tackled the problem of limited scalability in continuous-time diffusion network inference and influence estimation by developing a scalable framework (FIM) that approximates diffusion propagation from cascades, achieving superior scalability in experiments.
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.