A Weighted Hybrid Centrality for Identifying Influential Individuals in Contact Networks

In the context of infectious disease spread, one of the most challenging tasks is to identify influential nodes in the human contact networks. In recent years, the research community has acquired strong evidences of complex and heterogeneous connections and diverse patterns in a variety of real human contact networks. The heterogeneity of network topologies has a deep influence on understanding the spreading dynamics of disease. In the process of infection spread, network edges are the critical communication channels. Many existing approaches for identifying prominent nodes in such networks rely on node attributes. Further, in unweighted networks, all the edges are considered to be equal, which imposes an unrealistic assumption for epidemic spreading through frequent interactions between humans. Here, we present an edge weighting technique, named as, Weighted Hybrid Centrality left( {{W_{{C_H}}}} right), that takes into account multiple centrality indicators, including degree, k-shell, and eigenvector centrality, as well as the frequency of interactions be-tween any two users (nodes) which is considered as potential edge weight. To analyze the performance of {W_{{C_H}}}, we have utilized the weighted susceptible-infected-recovered (W-SIR) simulator and have carried out a comparative experiment on six real-world heterogeneous networks. The proposed technique outperforms the baseline methods with 0.067 to 0.641 averaged Kendall's τ score. This method is useful for modeling disease dynamics and identifying highly influential contacts by considering both node and edge properties.