Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (5): 116-120.

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Centrality for nodes in social networks

LIU Xin, LI Peng, LIU Jing, WANG Yadan   

  1. 1.College of Computer Science and Technology, Wuhan University of  Science and  Technology, Wuhan 430065, China
    2.Hubei Province Key Laboratory of Intelligent Information Processing and Real-time  Industrial System, Wuhan 430065, China
  • Online:2014-03-01 Published:2015-05-12

社交网络节点中心性测度

刘  欣,李  鹏,刘  璟,王娅丹   

  1. 1.武汉科技大学 计算机科学与技术学院,武汉 430065
    2.智能信息处理与实时工业系统湖北省重点实验室,武汉 430065

Abstract: In social networks, investigating node influence and influence maximization is an important issue and attracts great interest in the research community. In order to analyze its personal influence and potential influence, it proposes a Personal-Potential Influence(PPI) algorithm, which evaluates the weight of its k-shell, closeness centrality and betweenness centrality by considering the strength of relationship between nodes. The experimental results show that PPI has the higher accuracy in node influence and outperforms other algorithms.

Key words: node influence, influence maximization, social network, key node, centrality

摘要: 研究节点影响力以及扩大节点影响力的范围在社交网络传播中具有重大意义。为了综合分析节点自身影响力与其潜在影响力,提出了PPI(Personal-Potential Influence,PPI)算法,用介数中心性值,紧密中心性值及k-shell值加权来评估节点自身影响力,再通过节点间的相互影响来评估其潜在影响力。实验结果表明PPI算法在评估节点影响力上有较好的准确性。

关键词: 节点影响力, 响力最大化, 社交网络, 重要节点, 中心性