摘要
针对现有方法利用网络信息相对割裂,很难描述链接次数与相似性分数关系的问题,提出一种动态网络中的链接预测方法,用节点相似性分数和链接次数组合时间序列模型进行预测.首先通过社区演化,预测历史上各时间快照下节点间的相似性分数;然后用二元时间序列模型将其与各时间快照下节点间的事实链接次数相结合,判断下一个时间段内各节点对链接发生的可能性;最后在Weibo-Net-Tweet微博转发数据集上进行测试.实验结果表明,该方法至少提高了5%的预测准确度,证明了社区演化与链接预测之间的内在联系,验证了二元时间序列模型的有效性.
Aiming at the problem that the existing methods made use of the relative fragmentation of network information, and it was difficult to describe the relationship between link number and similarity score, we proposed a link prediction method in dynamic networks, which used time series combined from node similarities scores and link mumber to predict. Firstly, it predicted similarity scores of all time snapshots by community evolution. Secondly, nodes similarities were combined with real link number by binary time series model and the probability of link between all node pairs in next period was predicted. Finally, the test was carried out on a dataset forwarded by Weibo-Net-Tweet microblog. The experimental results show that the method improves the prediction accuracy by at least 5%, proves the intrinsic relationship between community evolution and link prediction, and verifies the effectiveness of binary time series model.
作者
魏晓辉
许国威
王兴旺
徐海啸
WEI Xiaohui;XU Guowei;WANG Xingwang;XU Haixiao(College of Computer Science and Technology,Jilin University,Changchun 130012,China;High Performance Computing Centre,Jilin University,Changchun 130012,China)
出处
《吉林大学学报（理学版）》
CAS
北大核心
2019年第3期583-590,共8页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61602205
51627805
61170004)
国家重点研发计划专项基金(批准号:2016YFB0201503
2016YFB0701101)
教育部高等学校博士学科点专项科研基金(批准号:20130061110052)
吉林省科技攻关计划重大科技招标专项基金(批准号:20160203008GX)和吉林省科技攻关计划重点科技攻关项目(批准号:20140204013GX)
关键词
链接预测
社区演化
时间序列
节点相似性
link prediction
community evolution
time series
node similarity
