Gu Weiwei, Gao Fei, Lou Xiaodan, Zhang Jiang
Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, People's Republic of China.
School of Systems Science, Beijing Normal University, Beijing, 100875, People's Republic of China.
Sci Rep. 2021 Mar 26;11(1):6967. doi: 10.1038/s41598-021-85826-x.
In this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.
在本文中，我们提出了基于图注意力的网络表示（GANR），它利用图注意力架构并将图结构作为监督学习信息。与基于节点分类的表示相比，GANR可用于学习任何给定图的表示。GANR不仅能够学习高质量的节点表示，在链接预测、网络可视化和节点分类方面取得有竞争力的性能，还能提取有意义的注意力权重，可应用于节点中心性测量任务。GANR可以识别领先的风险投资投资者，发现高被引论文，并在易感-感染-恢复模型中找到最有影响力的节点。我们得出结论，图中的链接结构不仅限于预测链接本身，一旦提供合适的学习算法，如图注意力网络表示（GANR），它能够以无监督的方式揭示潜在的节点信息。
