共引增强有向网络嵌入研究

Study on Co-citation Enhancing Directed Network Embedding

网络嵌入旨在将网络节点嵌入到一个低维向量空间且最大程度地保存原有网络的拓扑结构及其属性。相比无向网络,有向网络具有特殊的非对称传递性,可体现在节点之间的高阶相似度量中,如何较好地保存这一特性是当前有向网络嵌入研究的热点和难点。针对此问题,通过引入有向网络的共引网络,设计了共引信息的度量函数,给出了一种有向网络高阶相似度量指标融合共引信息的统一框架,提出了可以保存非对称传递性的共引增强的高阶相似保存网络嵌入模型(Co-Citation Enhancing High-Order Proximity preserved Embedding,CCE-HOPE)。在4个真实数据集上进行链路预测实验的结果表明,不同高阶相似度量指标下,不同比重共引信息对效果影响具有一般规律,因此可以给出比重的最佳取值范围;在此范围内,与现有方法相比,CCE-HOPE方法可有效提高链接预测的准确度。

Network embedding algorithms embed a network into a low-dimensional vector space where the structure and the inherent properties of the graph can be preserved to the greatest extent.Compared with undirected networks,directed networks have special asymmetric transitivity which can be reflected in the high-order similarity measurement between nodes.A hot spot and difficulty of current directed network embedding research is how to preserve this feature well.Aiming at this problem,this paper introduces the co-citation network of directed networks and designs a metric function of the co-introduction information.At the same time,a unified framework is created for fusing the co-citation information and the high-order similarity metrics of directed networks.Then,this paper proposes a co-citation enhancing high-order proximity preserved embedding method,called CCE-HOPE,which can preserve the asymmetric transitivity well.In experiments,the proposed model is evaluated on link prediction using four real data sets.The results show that under different high-order similarity metrics,the performance of different proportions of co-introduction information follows a general regularity,so the optimal range of the proportion can be determined.Compared with other state-of-the-art methods,the method can effectively improve the accuracy of link prediction when the proportion of co-introduction information is within the optimal range.