ISSN 1000-1239 CN 11-1777/TP

• 人工智能 •

### 基于复合关系图卷积的属性网络嵌入方法

1. (武汉大学计算机学院 武汉 430072) (yiqic16@whu.edu.cn)
• 出版日期: 2020-08-01
• 基金资助:
国家自然科学基金项目(61572376,91646206);国家电网有限公司科技项目(5700-202072180A-0-00-00)

### Exploiting Composite Relation Graph Convolution for Attributed Network Embedding

Chen Yiqi, Qian Tieyun, Li Wanli, Liang Yile

1. (School of Computer Science, Wuhan University, Wuhan 430072)
• Online: 2020-08-01
• Supported by:
This work was supported by the National Natural Science Foundation of China (61572376, 91646206) and the State Grid Technology Project (5700-202072180A-0-00-00).

Abstract: Network embedding aims at learning a low-dimensional dense vector for each node in the network. It has attracted much attention from researchers in recent years. Most existing studies mainly focus on modeling graph structure and neglect the attribute information. Though attributed network embedding methods take node attribute into account, the informative relations between nodes and their attributes are still under-exploited. In this paper, we propose a novel framework to employ the abundant relation information for attributed network embedding. To this end, we first present to construct the composite relations between the nodes and their attributes in attributed networks. We then develop a composite relation graph convolution network (CRGCN) to encode the composite relations in both types of networks. We conduct extensive experiments on real world datasets and results demonstrate the effectiveness of our model on various network analysis tasks.