A Novel Approach for Analyzing Temporal Influence Dynamics in Social Networks

Abstract

Features like assortative mixing; high clustering, short average path lengths, broad degree distributions, and community structure have been the subject of numerous recent social network studies. All of these features are met by the model that is introduced in this study. Additionally, our model enables interactions between various communities, fostering a rich network environment. The asymptotically scale-free degree distribution is maintained by our model, which achieves a high clustering coefficient. In our model, the community structure is generated by a mix of mechanisms involving implicit preferential attachment and random attachment. We expand our consideration to include neighbor of neighbor of Initial Contact (NNIC) as well, in contrast to earlier approaches that solely focused on neighbor of Initial Contact (NIC) as an implicit preferential contact. If a newly added vertex chooses more than one initial contact, this extension makes it possible for contacts between those initial contacts to occur. Consequently, our model facilitates the development of complex social networks beyond those used as basic references. Finally, we conduct centrality calculations on both the existing model and our developed model, providing a comparative analysis of the results.