PCANE:Preserving Context Attributes for Network Embedding

摘要：

　　Through mapping network nodes into low-dimensional vectors,network embedding methods have shown promising results for many downstream tasks,such as link prediction and node classification.Recently,attributed network embedding obtained progress on the network associated with node attributes.However,it is insufficient to ignore the attributes of the context nodes,which are also helpful for node proximity.In this paper,we propose a new attributed network embedding method named PCANE(Preserving Context Attributes for Network Embedding).PCANE preserves both network structure and the context attributes by optimizing new object functions,and further produces more informative node representations.PCANE++is also proposed to represent the isolated nodes,and is better to represent high degree nodes.Experiments on 3 real-world attributed networks show that our methods outperform the other network embedding methods on link prediction and node classification tasks.

作者: Danhao Zhu Xin-yu Dai Kaijia Yang Jiajun Chen Yong He

作者单位: Nanjing University,Nanjing 210031,Jiangsu,Peoples Republic of China;Jiangsu Police Institute,Nanjin Nanjing University,Nanjing 210031,Jiangsu,Peoples Republic of China Jiangsu Police Institute,Nanjing 210093,Jiangsu,Peoples Republic of China

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 156-168

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）