位置修复和粒子置换的FSUD-PSO签名网络社区发现
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摘要
为提高签名网络社区发现效果,解决其评估指标存在的数据耦合和依赖性,造成网络社区单指标优化存在较大局限性的问题,提出了基于位置修复和粒子置换的FSUD-PSO(fast sorting and uniform density of multi-objective particle swarm optimization)签名网络社区发现算法。首先,对签名网络模型进行研究,并在考虑数据耦合和依赖性前提下给出签名网络社区评价指标,以及多目标Pareto最优目标模型;其次,构建签名网络模型的多目标优化粒子编码与更新规则,并根据签名网络特点设计了位置修复和粒子置换策略,同时为提高多目标粒子群算法性能,设计了快速排序均匀密度的多目标粒子群算法FSUD-PSO;最后,通过标准测试集实验对比,验证了所提FSUD-PSO签名网络社区发现算法的有效性。
In order to improve the effect of signature network community discovery,and solve the evaluation indicator of the presence of data coupling and dependence,which leads some limitations of single index optimization in network community,this paper proposes signature network community discovery based on FSUD-PSO(fast sorting and uniform density of multi-objective particle swarm optimization) with location repair and particle replacement.Firstly,this paper studies the signature network model,and gives the community evaluation index of the signature network under the premise of considering the data coupling and dependence.Secondly,this paper builds a signature network model with particle coding and update rules for multi-objective optimization and network according to the characteristics of signature design repair and particle replacement,at the same time,in order to improve multi-objective particle swarm algorithm performance,it designs the FSUD-PSO algorithm.Finally,the effectiveness of the proposed FSUD-PSO signaturenetwork community is verified by comparing with the standard test sets.
In order to improve the effect of signature network community discovery,and solve the evaluation indicator of the presence of data coupling and dependence,which leads some limitations of single index optimization in network community,this paper proposes signature network community discovery based on FSUD-PSO(fast sorting and uniform density of multi-objective particle swarm optimization) with location repair and particle replacement.Firstly,this paper studies the signature network model,and gives the community evaluation index of the signature network under the premise of considering the data coupling and dependence.Secondly,this paper builds a signature network model with particle coding and update rules for multi-objective optimization and network according to the characteristics of signature design repair and particle replacement,at the same time,in order to improve multi-objective particle swarm algorithm performance,it designs the FSUD-PSO algorithm.Finally,the effectiveness of the proposed FSUD-PSO signaturenetwork community is verified by comparing with the standard test sets.
引文
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[14]Gomez S,Jensen P,Arenas A.Analysis of community structure in networks of correlated data[J].Physical Review E:Statistical Nonlinear&Soft Matter Physics,2009,80:016114.
[15]Feng Lin,Wang Jing,Liu Shenglan.Multi-label dimensionality reduction and classification with extreme learning machines[J].Journal of Systems Engineering and Electronics,2014,25(3):502-513.
[1]于海,赵玉丽,崔坤,等.一种基于交叉熵的社区发现算法[J].计算机学报,2015,38(8):1574-1581.
[4]张英杰,龚中汉,陈乾坤.基于免疫离散差分进化算法的复杂网络社区发现[J].自动化学报,2015,41(4):749-757.
[6]辛宇,杨静,谢志强.基于数据场分析的语义重叠社区发现算法[J].中国科学:信息科学,2015,45(7):918-933.
[10]邱晓辉,陈羽中.一种面向社会网络社区发现的改进粒子群优化算法[J].小型微型计算机系统,2014,35(6):1422-1426.
[11]黄发良,张师超,朱晓峰.基于多目标优化的网络社区发现方法[J].软件学报,2013,24(9):2062-2077.
[2]Folino F,Pizzuti C.An evolutionary multiobjective approach for community discovery in dynamic networks[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(8):1838-1852.
[3]Wang Changdong,Lai Jianhuang,Yu P S.NEIWalk:community discovery in dynamic content-based networks[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(7):1734-1748.
[4]Zhang Yingjie,Gong Zhonghan,Chen Qiankun.A complex network community discovery based on immune discrete differential evolution algorithm[J].Journal of Automation,2015,41(4):749-757.
[5]Zhang Bentao,Li Yong,Jin Depeng,et al.Social-aware peer discovery for D2D communications underlaying cellular networks[J].IEEE Transactions on Wireless Communications,2015,14(5):2426-2439.
[6]Xin Yu,Yang Jing,Xie Zhiqiang.An algorithm for semantic overlapping community discovery based on data field analysis[J].Chinese Science:Information Science,2015,45(7):918-933.
[7]Zhang Jiankang,Chen Sheng,Mu Xiaomin,et al.Evolutionary-algorithm-assisted joint channel estimation and turbo multiuser detection/decoding for OFDM/SDMA[J].IEEE Transactions on Vehicular Technology,2014,63(3):1204-1222.
[8]Liu Qiang,Zhou Bin,Li Shudong,et al.Community detection utilizing a novel multi-swarm fruit fly optimization algorithm with hill-climbing strategy[J].Arabian Journal for Science and Engineering,2016,41(3):807-828.
[9]Wu Chaotian,Li Tianrui,Teng Fei,et al.An improved PSO algorithm for community detection[C]//Proceedings of the 2015 10th International Conference on Intelligent Systems and Knowledge Engineering,Taipei,China,Nov24-27,2015.Piscataway,USA:IEEE,2015:138-143.
[10]Qiu Xiaohui,Chen Yuzhong.An improved particle swarm optimization algorithm for social network community discovery[J].Journal of Chinese Computer System,2014,35(6):1422-1426.
[11]Huang Faliang,Zhang Shichao,Zhu Xiaofeng.A network community discovery method based on multi objective optimization[J].Journal of Software,2013,24(9):2062-2077.
[12]Gong Maoguo,Ma Lijia,Zhang Qingfu,et al.Community detection in networks by using multiobjective evolutionary algorithm with decomposition[J].Physica A:Statistical Mechanics and Its Applications,2012,391(15):4050-4060.
[13]Cao Cen,Ni Qingjian,Zhai Yuqing.A novel community detection method based on discrete particle swarm optimization algorithms in complex networks[C]//Proceedings of the 2015 IEEE Congress on Evolutionary Computation,Sendai,May 25-28,2015.Piscataway,USA:IEEE,2015:171-178.
[14]Gomez S,Jensen P,Arenas A.Analysis of community structure in networks of correlated data[J].Physical Review E:Statistical Nonlinear&Soft Matter Physics,2009,80:016114.
[15]Feng Lin,Wang Jing,Liu Shenglan.Multi-label dimensionality reduction and classification with extreme learning machines[J].Journal of Systems Engineering and Electronics,2014,25(3):502-513.
[1]于海,赵玉丽,崔坤,等.一种基于交叉熵的社区发现算法[J].计算机学报,2015,38(8):1574-1581.
[4]张英杰,龚中汉,陈乾坤.基于免疫离散差分进化算法的复杂网络社区发现[J].自动化学报,2015,41(4):749-757.
[6]辛宇,杨静,谢志强.基于数据场分析的语义重叠社区发现算法[J].中国科学:信息科学,2015,45(7):918-933.
[10]邱晓辉,陈羽中.一种面向社会网络社区发现的改进粒子群优化算法[J].小型微型计算机系统,2014,35(6):1422-1426.
[11]黄发良,张师超,朱晓峰.基于多目标优化的网络社区发现方法[J].软件学报,2013,24(9):2062-2077.