發(fā)布時間：2018-04-04 07:41

  本文選題：進化聚類　切入點：標簽傳播　出處：《軟件學報》2017年07期

【摘要】：社團的數(shù)目和時間平滑性的平衡因子一直是基于進化聚類的動態(tài)網(wǎng)絡社團發(fā)現(xiàn)算法的最大的問題.提出一種基于標簽的多目標優(yōu)化的動態(tài)網(wǎng)絡社團發(fā)現(xiàn)算法(LDMGA).借鑒多目標遺傳算法思想,將進化聚類思想轉換為多目標遺傳算法優(yōu)化問題,在保證當前時刻的聚類質量的同時,又能使當前聚類結果與前一時刻網(wǎng)絡結構保持一致.該算法在初始化過程中加入標簽傳播算法,提高了初始個體的聚類質量.提出基于標簽的變異算法,增強了算法的聚類效果和算法的收斂速度.同時,多目標遺傳算法和標簽算法的結合使算法可擴展性更強,運行時間隨著節(jié)點或者邊數(shù)目的增加呈線性增長.將該算法與目前的優(yōu)秀算法在仿真數(shù)據(jù)集和真實數(shù)據(jù)集上進行對比實驗,結果表明,該算法既有良好的聚類效果,又有良好的擴展性.
[Abstract]:The balance factor of community number and time smoothness is the biggest problem of dynamic network community discovery algorithm based on evolutionary clustering.A dynamic community discovery algorithm based on label based multi-objective optimization is proposed.Using the idea of multi-objective genetic algorithm for reference, the idea of evolutionary clustering is transformed into an optimization problem of multi-objective genetic algorithm, which can ensure the clustering quality at the present time and at the same time make the current clustering result consistent with the network structure of the previous time.In the process of initialization, tag propagation algorithm is added to the algorithm to improve the clustering quality of the initial individuals.A label-based mutation algorithm is proposed to improve the clustering effect and convergence speed of the algorithm.At the same time, the combination of multi-objective genetic algorithm and label algorithm makes the algorithm more scalable, and the running time increases linearly with the increase of the number of nodes or edges.The results show that the algorithm not only has good clustering effect, but also has good expansibility.
【作者單位】： 電子科技大學計算機科學與技術學院;上海交通大學電子信息與電氣工程學院;電子科技大學信息與軟件工程學院;
【基金】：國家自然科學基金(61300192) 國家科技支撐計劃(2013BAH33F02) 中央高校基本科研業(yè)務費(ZYGX2014J052) 四川省科技支撐計劃(2015GZ0096)~~
【分類號】：TP18;TP311.13

【參考文獻】

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本文編號：1709006