The analysis of key nodes in complex social networks

Abstract

Key nodes play really important roles in the complex socail networks. It’s worthy of analysis on them so that the social network is more intelligible. After analyzing several classic algorithms such as degree centrality, betweenness centrality, PageRank and so forth, there indeed exist some deficiencies such as ignorance of edge weights, less consideration on topology and high time complexity in the research on this area. This paper makes three contributions to address these problems. Firstly, a new idea, divide and conquer, is introduced to analyze directed-weighted social networks in different scales. Secondly, the improved degree centrality algorithm is proposed to analyze small-scale social networks. Thirdly, an algorithm named NodeRank is proposed to address large-scale social networks based on PageRank. Subsequently, the effectiveness and feasibility of these two algorithms are demonstrated respectively with case and theory. Finally, two representative basesets with respect to the social networks are adopted to mine key nodes in contrast to other algorithms. And experiment results show that the algorithms presented in this paper can preferably mine key nodes in directed-weighted complex social networks.