A Novel Multi-Indicator Evaluation Algorithm for Identifying the Important Nodes in Complex Networks

Abstract

Identification of important nodes is an emerging hot topic in complex networks over the last few years. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness, closeness, etc. At present, most algorithms of important node evaluation are based on the single-indicator, which can't reflect the whole condition of the complex network. Therefore, in this paper, after choosing multiple indicators from degree centrality, closeness centrality, eigenvector centrality, information centrality, density/clustering coefficient, mutual-information centrality, etc., and a new multi-indicator evaluation algorithm based on Locally Linear Embedding (LLE) for identifying important nodes in complex network is proposed. This proposed algorithm is compared with some single-indicator algorithms and other mainstream multi-indicator algorithms based on real-world networks. Through comprehensive analysis, the experimental results show that the proposed method performs quite well in evaluating the importance of nodes, and it is rational, effective, integral and accurate.