Influence propagation in social networks: Interest-based community ranking model

The driving force behind content dissemination in Social network (SN) is the users' interest in the content, which is strongly reflected in their interactions. Obviously, user interest varies with the disseminated content. Consequently, the dynamic interest results in decomposing SN into dynamic user clusters interest groups. The objective of this work is to rank interest-based communities using influence propagation. The contribution of this work is threefold: First, to highlight the significance of the indirect influence among interest-based user groups. Second, to study its impact on content dissemination capability. Third, to propose an ultimate ranking model (UltRank) that uniquely considers direct and indirect influences which are reflected in a new reachability metric that considers: 1. Distance among interest groups. 2. Percentage of reachable interest groups. 3. Percentage of reachable nodes. UltRank model has been evaluated in comprehensive experiments. First, clustering quality perspective, the Silhouette coefficient for the identified interest groups is on average 0.996 and the Jaccard coefficient of 97% of different interest groups members equals 0. Second, ranking capability perspective, UltRank model can rank up to 91% of interest groups in SN. Finally, ranking effectiveness perspective, UltRank ranking list has a competing network coverage results against the other benchmark approaches. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

Automated summary

The paper focuses on ranking interest-based communities using influence propagation and explores the impact of indirect influence on content dissemination capability. The UltRank model considers factors such as reachability among interest groups.