Multiscale Clustering Based Diffusion Representation Learning Method

Abstract

Information diffusion model aims to understand the process of information diffusion in the network. Currently, state-of-the-art methods utilize vector representation of users to encode these factors. Apart from personal factors, decisions of others of the local community can also affect a user’s decision on propagation. Recently, a multiscale information diffusion model called HID applies hierarchical clustering to improve the performance of many existing diffusion models. Though extensive experiments have proven the effectiveness of the model, it fails to encode diffusion time into the representation space, and the adopted clustering algorithms are independent of the diffusion model. Thus, we propose a multiscale clustering based diffusion representation method that incorporates diffusion time into diffusion proximity matrix and adopts a hierarchical clustering method suitable for multiscale diffusion learning. Experiments show the effectiveness of the proposed method.