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.
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Acknowledgements
This work is supported in part by the Foundation of Graduate Innovation Center in Nanjing University of Aeronautics and Astronautics under Grant No. kfjj20161601, the National Natural Science Foundation of China under Grant No. 61672022, Key Disciplines of Computer Science and Technology of Shanghai Polytechnic University under Grant No. XXKZD1604, the Fundamental Research Funds for the Central Universities and Foundation of Graduate Innovation of Shanghai Polytechnic University.
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Pan, Y., Tan, W., Chen, Y. (2017). The Analysis of Key Nodes in Complex Social Networks. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_74
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