计算机科学
水准点(测量)
嵌入
节点(物理)
人工智能
特征学习
有符号图
图形
理论计算机科学
代表(政治)
特征(语言学)
链接(几何体)
机器学习
工程类
哲学
大地测量学
政治
法学
地理
结构工程
语言学
计算机网络
政治学
作者
Junjie Huang,Huawei Shen,Liang Hou,Xueqi Cheng
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2021-05-18
卷期号:35 (1): 196-203
被引量:21
标识
DOI:10.1609/aaai.v35i1.16093
摘要
Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related socio- logical theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model’s effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embeddings. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods.
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