Identification of the most influential nodes involving all topological dimensions of a network

Biological systems are composed of highly complex networks, and decoding the functional significance of individual network components is critical for understanding healthy and diseased states. Several algorithms have been designed to identify the most influential nodes within a network. However, current methods do not address all the topological dimensions of a network or correct for inherent positional biases, which limits their accuracy. Here we present Integrated Value of Influence (IVI), an algorithm that integrates the most important and commonly used network centrality measures in an unbiased way and captures all of the topological dimensions of a network to successfully identify the most influential nodes. The evaluation of IVI in the context of both simulated and experimental data confirmed their superiority to other respective contemporary methods and algorithms. Altogether, IVI is a versatile algorithm that could help all network researchers in the identification of the most influential players in the entire system.