Dynamic social network analysis: A novel approach using agent‐based model, author‐topic model, and pretopology

Abstract

SummaryWe propose in this work a novel approach for dynamic social network analysis by combining an agent‐based model, an author‐topic model, and pretopology. We first introduce an analytical model for a dynamic social network associated with textual content using agent‐based and author‐topic models, namely, Textual‐ABM. The purpose of Textual‐ABM is to support for the concept exploitation of the “dynamics” of a social network, which contains not only network's structure transformation but also agent's interest variation over time. Agent's interest is revealed through topic probability distribution, which is estimated based on textual data using an author‐topic model. In addition to demonstrating the fluctuation of the social network related to textual content, we also exploit information propagation phenomena by proposing two expanded spreading models. The first model is an expanded model of an independent cascade model in which probability of infection is formed on homophily, namely H‐IC. We have implemented experiments on a collected dataset from the Neural Information Processing Systems Conference and have acquired satisfying results. Furthermore, we propose an extended model of pretopological cascade model from our previous work, namely, Textual‐PCM. The advantage of PCM comparison with classical cascade model is to utilize pseudoclosure function built from pretopology to define the more complex set of neighborhoods. In this work, we expand PCM to apply detail for a social network related to textual information. A toy example with some experiments and discussion is illustrated for Textual‐PCM. The work in this paper is an extended version of our paper dynamic social network analysis using author‐topic model presented in I4CS 2018 Conference.