计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 211-217.doi: 10.11896/jsjkx.201200231
赵曼, 赵加坤, 刘金诺
ZHAO Man, ZHAO Jia-kun, LIU Jin-nuo
摘要: 链路预测是网络分析与挖掘领域中备受关注的研究方向。链路预测算法所预测的网络中的缺失连接实际上是一种数据挖掘的过程,而推断的将来可能产生的连接则与网络的发展演化相关。因此,如何提高链路预测的精确度是一项有意义且具有挑战性的研究。基于自我中心网络分解和社区聚类的最新研究,提出一种基于自我中心网络结构特征和网络表示学习的链路预测算法(Ego-Embedding)。Ego-Embedding将原网络转换成角色图,再结合网络的微观结构信息和上下文信息重构嵌入过程,为每一个节点学习一个或多个向量表示,使向量表示更准确地描述网络节点信息,从而提高链路预测的精确度。在3个公开数据集(Facebook,PPI-Yeast和ca-HepTh)上进行实验仿真,并使用AUC作为评价指标,仿真结果表明,算法Ego-Embedding的表现均优于5个实验对比方法(CN,AA,Node2vec,M-NMF和Splitter),且最高将链路预测的错误率减少了约47%。
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