Influential nodes detection in complex networks via diffusion Fréchet function

Abstract

Identifying influential nodes in a complex network is an important graph mining problem with many diverse applications such as information propagation, market advertising, and rumor controlling. Influential nodes in a network play critical roles and largely affect network structure and functions. They can diffuse or spread information throughout a complex network more rapidly. Various methods have been developed to identify important nodes to address the influential node detection problem. In this paper, we use the diffusion Fréchet function (DFF), a function that leverages network topology and is robust to noise in data, to identify the most influential nodes in networks. We apply our method to various real-world networks. We then compare its performance to the classical graph-theoretic centrality measures using the Susceptible-Infected-Recovered (SIR) simulation model. Our experimental results suggest that our method is promising in influential node detection and more effective than the classical centrality measures.