Application of m-Ranking to Node Analysis in Complex Networks

Abstract

Identifying influential nodes in complex networks has gained considerable attention because of its significant theoretical importance and broad range of applications. The m-Ranking method incorporates both the degree of nodes and the weights of edges to rank nodes with varying importance levels. This approach can also be applied to unweighted networks by simply setting the weight parameter $\alpha= 1$. Furthermore, we illustrate the effectiveness of the proposed method using a real-world air traffic network, where the rankings produced are shown to be more meaningful compared to those obtained through existing methods.