Comparative Evaluation of Centrality Measures for Detecting Significant Nodes in Social Networks

Social networks are certainly a crucial platform for bringing people together globally. Detecting the significant nodes inside the social network remains an open problem because of the broad variety of network sizes. To solve this problem, different centrality measures have been introduced. Detecting the significant nodes is essential for speeding up or slowing down the spread of information, managing diseases and rumors, and more. This paper presents a comparative evaluation of 12 centrality measures to determine the most effective measure on the basis of accuracy, differentiation capability, and runtime. To validate performance, a series of experiments is conducted on four social networks using the validation metrics such as monotonicity, the SIR model, and Kendall tau. The experimental outcomes indicate that the gravity-based measures have superior accuracy and differentiation capability as compared to other measures. Finally, this paper outlines future research directions for enhancements based on centrality measures.