Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (10): 2971-2975.DOI: 10.11772/j.issn.1001-9081.2018030592

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HK extended model with tunable degree correlation and clustering coefficient

ZHOU Yujiang, WANG Juan   

  1. College of Information Engineering, Shenzhen University, Shenzhen Guangdong 518060, China
  • Received:2018-03-22 Revised:2018-04-29 Online:2018-10-10 Published:2018-10-13
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Guangdong Province (2015A030313552).


周玉江, 王娟   

  1. 深圳大学 信息工程学院, 广东 深圳 518060
  • 通讯作者: 王娟
  • 作者简介:周玉江(1981-),男,湖北枣阳人,硕士研究生,主要研究方向:社交网络、机器学习;王娟(1979-),女,广东深圳人,副教授,博士,主要研究方向:社交网络、无线通信网络。
  • 基金资助:

Abstract: Concerning the problem that most of the existing social network growing models have negative degree correlation, considering the characteristics of positive degree correlations and high clustering coefficients, a new social network growing model was proposed based on Holme and Kim (HK) model. Firstly, the topological structure of a real-world social network was analyzed to obtain some important topological parameters of real social networks. Secondly, the HK model was improved by introducing triad formation mechanism, namely HK extended model with Turnable Degree Correlation and Clustering coefficient (HK-TDC&C), by which both clustering coefficients and degree correlations in the network could be adjusted. The model could be used to construct social networks with various topological properties. Finally, using mean field theory, the degree distribution of the model was analyzed, and Matlab was used for numerical simulation to calculate other topological parameters of the network. The results show that, by turning preferred attachment parameters and connection probabilities, the social network constructed by HK-TDC&C model can satisfy the basic characteristics of social networks, including scale-free characteristics, small world characteristics, high clustering coefficient characteristics and degree positive correlation properties, and its topology is closer to the real social network.

Key words: social network, evolution model, power law distribution, degree correlation, HK model

摘要: 现有的社交网络增长演化模型的度相关性大多为负值。针对这种情况,以HK(Holme和Kim)模型为基础,考虑社交网络中度的正相关特性以及高聚类系数的特征,提出一种适用于构造社交网络的演化增长模型。首先,对现实中的社交网络拓扑结构进行分析,获取真实社交网络的一些重要拓扑参数;然后,通过引入改进的三角连接机制,对HK模型进行改进以实现网络的聚类系数和相关性均可调的目的,称其为聚类系数和度相关性均可调的HK扩展模型(HK-TDC&C),通过该模型可以构造各种拓扑结构的网络。最后,利用平均场理论对该模型的度分布进行分析,并采用Matlab进行数值仿真,计算网络的其他拓扑参数。实验结果表明:通过调节择优参数和连接概率,用HK-TDC&C构造的社交网络可以满足社交网络的基本特性:无标度特性、小世界特性、高聚类系数特性、度正相关特性,其拓扑结构更接近真实社交网络。

关键词: 社交网络, 演化模型, 幂律分布, 度相关性, HK模型

CLC Number: