LAUSR

The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.