摘要
基于改进蚁群优化算法与子图演化,提出了一种新型非监督社交网络链路预测(SE-ACO)方法。该方法首先在社交网络图中确定特殊子图;然后研究子图演化以预测图中的新链接,并用蚁群优化算法定位特殊子图;最后针对所提方法使用不同网络拓扑环境与数据集进行检验。结果表明,与其他无监督社交网络预测算法相比,所提SE-ACO方法在多数数据集上的评估结果较好,且运行时间较短,这表明图形结构在链路预测算法中起重要作用。
Based on improved ant colony algorithm and subgraph evolution fusion,a new unsupervised social network link prediction method(SE-ACO)was proposed.First,the special subgraph was determined in the social network graph.Then the evolution of the subgraph was studied to predict the new links in the graph,and the special subgraph was located by the ant colony method.Finally,using different network topology environments and data sets to test the proposed method.Compared with other unsupervised social network prediction algorithms,the proposed SE-ACO method has the best evaluation results,shorter running time and the best effect on most data sets,which indicates that graph structure plays an important role in link prediction algorithm.
作者
顾秋阳
琚春华
吴功兴
GU Qiuyang;JU Chunhua;WU Gongxing(School of Management,Zhejiang University of Technology,Hangzhou 310023,China;China Institute for Small and Medium Enterprises,Zhejiang University of Technology,Hangzhou 310023,China;Business School,University of Nottingham Ningbo,Ningbo 315175,China;School of Management Science&Engineering,Zhejiang Gongshang University,Hangzhou 310018,China)
出处
《通信学报》
EI
CSCD
北大核心
2020年第12期21-35,共15页
Journal on Communications
基金
国家自然科学基金资助项目(No.71571162)
浙江省社科规划重点课题基金资助项目(No.20NDJC10Z)
浙江省自然科学基金资助项目(No.LQ20G010002)。
关键词
链路预测
蚁群优化算法
社交网络
子图演化
link prediction
ant colony optimization algorithm
social network
subgraph evolution
