Social Network User Influence Dynamics Prediction

Abstract

Identifying influential users and predicting their “network impact” on social networks have attracted tremendous interest from both academia and industry. Most of the developed algorithms and tools are mainly dependent on the static network structure instead of the dynamic diffusion process over the network, and are thus essentially based on descriptive models instead of predictive models. In this paper, we propose a dynamic information propagation model based on Continuous-Time Markov Process to predict the influence dynamics of social network users, where the nodes in the propagation sequences are the users, and the edges connect users who refer to the same topic contiguously on time. Our proposed model is compared with two baselines, including a well-known time-series forecasting model – Autoregressive Integrated Moving Average and a widely accepted information diffusion model – Independent cascade. Experimental results validate our ideas and demonstrate the prediction performance of our proposed algorithms.