基于时变差别适应度的网络演化模型
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摘要
将节点适应度的时变性和差异性抽象为时变差别适应度,在适应度模型的基础上,提出一种改进的网络演化模型。网络中新加入的节点趋向于连接节点入度大及感兴趣的节点,节点在演化过程中会随时与其他节点进行连接和断开。基于此,综合优先连接、随机加边、随机减边、节点互粉等机制实现网络演化。通过仿真分析节点的时变性和差异性对网络演化的影响,结果表明,该模型生成的网络度分布呈幂律分布,具有小世界现象,且与真实网络拟合度较高,验证了模型的正确性和有效性。
Time-varying performance and differentiation of node fitness are abstracted into time-varying difference fitness,and an improved network evolution model based on the fitness is proposed. In the network,the newly joined nodes tend to connect some nodes that have larger degree or attraction. And in the evolution process,the nodes are connected and disconnected with other nodes at any time. On this basis,a series of mechanisms including preferential attachment,random add edges,random delete edges and the nodes' mutual fans are used to achieve the evolution of the network. The influence of node time-varying performance and differentiation on network evolution is analysed seperatly.Through simulation analysis,the model of the distribution follows a power lawdistribution and with a small world phenomenon,and has high degree of fitting with the real network. The result verifies the correctness and validity of the model.
Time-varying performance and differentiation of node fitness are abstracted into time-varying difference fitness,and an improved network evolution model based on the fitness is proposed. In the network,the newly joined nodes tend to connect some nodes that have larger degree or attraction. And in the evolution process,the nodes are connected and disconnected with other nodes at any time. On this basis,a series of mechanisms including preferential attachment,random add edges,random delete edges and the nodes' mutual fans are used to achieve the evolution of the network. The influence of node time-varying performance and differentiation on network evolution is analysed seperatly.Through simulation analysis,the model of the distribution follows a power lawdistribution and with a small world phenomenon,and has high degree of fitting with the real network. The result verifies the correctness and validity of the model.
引文
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[3]Price D J.Networks of Scientific Papers[J].Science,1965,149(3683):510-515.
[4]Bianconi G,Barabási A L.Bose-einstein Condensation in Complex Netw orks[J].Physical Review Letters,2001,86(24):5632-5635.
[5]van Raan A F J.On Growth,Ageing,and Fractal Differentiation of Science[J].Scientometrics,2000,47(2):347-362.
[6]Albert R,Barabási A L.Statistical Mechanics of Complex Netw orks[J].Review s of M odern Physics,2002,74(2):47-97.
[7]蒋品群,罗晓曙,汪秉宏.一个新的演化网络模型中节点的吸引力研究[J].广西师范大学学报(自然科学版),2009,27(4):15-18.
[8]Holme P,Saramki J.Temporal Networks[J].Physics Reports,2012,519(3):97-125.
[9]Dorogovtsev S N,Mendes J F F,Samukhin A N.Structure of Growing Networks with Preferential Linking[J].Physical Review Letters,2000,85(21):4633-4636.
[10]陶少华,杨春,李慧娜.基于节点吸引力的复杂网络演化模型研究[J].计算机工程,2009,35(1):111-113.
[11]田生文,杨洪勇,李阿丽,等.基于聚类效应节点吸引力的复杂网络模型[J].计算机工程,2010,36(10):58-60.
[12]王瑞丽,蔡国永,林航.一种新的在线社会网络演化模型[J].计算机工程,2012,38(23):71-74.
[13]杨俊涛.基于兴趣值的适应度模型的研究[J].西南师范大学学报(自然科学版),2014,34(3):112-116.
[14]王晶,朱珂,汪斌强.基于用户社会属性及行为特征吸引度的微博粉丝网络演化模型[J].计算机应用,2013,33(10):2753-2756.
[15]袁博阳.基于微博内容和用户关注的微博用户兴趣模型构建[D].广州:华南理工大学,2015.
[16]雷兵,刘维.基于微博关注推荐服务的用户兴趣模型研究[J].情报科学,2015,33(9):126-130.
[17]杨福强.基于微博的个性化推荐问题研究[D].济南:山东师范大学,2015.
[18]徐晓彤.微博用户兴趣模型及个性化推荐技术研究与应用[D].大连:大连海事大学,2015.
[19]Myers S A,Zhu C,Leskovec J.Information Diffusion and External Influence in Netw orks[C]//Proceedings of the 18th ACM SIGKDD International Conference on Know ledge Discovery and Data M ining.New York,USA:ACM Press,2012:33-41.
[20]Liang Hong,Lu Gang,Xu Nanshan.Analyzing User Influence of M icroblog[C]//Proceedings of the 5th International Conference on Advanced Computational Intelligence.Washington D.C.,USA:IEEE Press,2012:15-22.
[21]汪小帆.网络科学导论[M].北京:高等教育出版社,2012.