Evaluating the Vulnerability of Communities in Social Networks by Gravity Model

TL;DR

This paper introduces a gravity-based model to evaluate community vulnerability in social networks by considering internal structure, local interactions, and large-scale influences, providing a comprehensive assessment method validated on real networks.

Contribution

The paper proposes a novel gravity model incorporating multiple community aspects for vulnerability evaluation, extending beyond traditional structural-only approaches.

Findings

Model effectively captures multifaceted community vulnerability.

Validation on real networks demonstrates accuracy and practicality.

Sensitivity analysis confirms robustness of the weighting parameters.

Abstract

With the development of network science, the various properties of complex networks have recently received extensive attention. Among these properties, the vulnerability of the communities (VoCs) is particularly important. In the conventional research, only parts of structural features of the community rather than multiple aspects are considered in the evaluating model. However, in reality, the impact on the VoC is multifaceted, not only its own structure property, but also the influence of other communities. In order to better model the influence between communities, so as to evaluate the VoCs in the social network, a gravity-based community vulnerability evaluation model is proposed in this paper. In this proposed model, three different aspects of the factor are considered, i.e. the number of edges inside the community, the number of edges connected neighboring communities, and the gravity index (GI) of each community, which correspond to the interior information, small scale interaction relationship, and large scale interaction relationship of communities. By means of the Jensen-Shannon divergence (JSD) and log-sigmoid transition (LST) function, the abstract distance (AD) between each pair of communities can be calculated to construct the community network (CN). With the usage of gravity model, the GI of each community which describes the large scale interaction relationship can be obtained. Eventually, the community vulnerability degree and order can be calculated by this proposed model, and the sensitivity of weighting parameters is analyzed by Sobol' indices. In particular, this proposed method can degenerate to the classical method with the setting of weighting parameters. The effectiveness and reasonability of this proposed model are demonstrated by several real world complex networks.