TriBeC: identifying influential users on social networks with upstream and downstream network centrality

Abstract

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.

KEYWORDS:

• online social networks
• information propagation
• network centrality
• network quartiles
• social interconnections
• data-driven modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Code availability

The research code will be made available on request from readers.

Availability of data and material

Research data associated with this paper is extracted through web-crawling and API-based programming. Hence, the dataset will be made available on-request from readers.