HellRank: A Hellinger-based Centrality Measure for Bipartite Social Networks.*

S.M. Taheri, H. Mahyar, M. Firouzi, E.K. Ghalebi, R. Grosu, and A. Movaghar.

Measuring centrality in a social network, especially in bipartite mode, poses many challenges. For example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node use local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.

To appear in SNAM'17, the Social Network Analysis and Mining Journal, 2017, Springer.

*This work was partially supported by the Artemis EMC2 Award, the NSF-Frontiers Cyber-Physical Heart Award, FWF-NFN RiSE Award, FWF-DC LMCS Award, FFG Harmonia Award, FFG Em2Apps Award, and the TUW CPPS-DK Award.