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The Analysis of Key Nodes in Complex Social Networks

机译：复杂社交网络中的关键节点分析

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摘要

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, Page Rank 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.
机译：关键节点在复杂的社交网络中起着非常重要的作用。值得对它们进行分析，以便使社交网络更清晰易懂。在分析了诸如度中心性，中间性中心性，页面等级等几种经典算法之后，在该领域的研究中确实确实存在一些缺陷，例如边缘权重的无知，对拓扑的考虑较少以及时间复杂性高。本文为解决这些问题做出了三点贡献。首先，引入了“分而治之”的新思想来分析不同规模的定向加权社交网络。其次，提出了一种改进度中心度算法来分析小型社交网络。第三，提出了一种基于PageRank的大规模社交网络NodeRank算法。随后，分别用实例和理论分别论证了这两种算法的有效性和可行性。最后，与其他算法相比，采用了关于社交网络的两个代表性基集来挖掘关键节点。实验结果表明，本文提出的算法能够较好地挖掘定向加权复杂社交网络中的关键节点。

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