Concept Stability Entropy: A Novel Group Cohesion Measure in Social Networks

Abstract

Group cohesion is regarded as a central group property across both social psychology and sociology. It facilities the understanding of the organizational behavior of users, and in turn guides the users to work well together in order to achieve goals within a social network. Therefore, group cohesion assessment is a crucial research issue for social network analysis. Group cohesion is often viewed as network density in the current state-of-the-art. Due to the advantages of characterizing the cohesion of a network with concept stability, this article presents a novel group cohesion measure, called concept stability entropy inspired by Shannon Entropy. Particularly, the scale of concept stability entropy is investigated. Considering the dynamic nature of social networks, an incremental algorithm for concept stability entropy computation is devised. In addition, we explore the correlation between concept stability entropy and other related metrics, i.e., network density, average degree, and average clustering coefficient. Extensive experimental results first validate that the concept stability entropy falls into the range of $[0, log(k)]$ ( $k$ is the number of formal concepts), and then demonstrate that the concept stability entropy has a positive correlation with the average degree and a negative correlation with the network density and average clustering coefficient. Practically, a case study on the COVID-2019 virus network is conducted for illustrating the usefulness of our proposed group cohesion assessment approach.