Influential Node Detection in Online Social Networks Using Skill Features of User Profiles

The explosive growth of online social networks (OSNs) has led to the emergence of a variety of communities, where users can share their interests, skills, and knowledge. In this context, identifying the most influential nodes is of paramount importance, as these nodes can impact the community's behavior and evolution. An innovative method for identifying influential nodes is proposed in this research using skill-based user profiles of different communities of OSNs. The proposed method employs a unique pagerank algorithm to calculate skill-based centrality in order to determine which node in the network has the greatest influence. Through trials on real-world datasets of LinkedIn user profiles, the method's efficacy is shown.