论文标题
基于自我发项网络的节点嵌入模型
A Self-Attention Network based Node Embedding Model
论文作者
论文摘要
尽管最近有几个进展迹象，但对归纳环境进行了有限的研究，在该环境中，新见到的节点需要嵌入 - 在图形网络深度学习的实际应用中通常遇到的设置。这显着影响下游任务的性能，例如节点分类，链接预测或社区提取。为此，我们提出了一种新颖的无监督嵌入模型的Sanne，其中心思想是在随机步行中使用变压器自我发项网络进行迭代汇总的节点矢量表示。我们的Sanne旨在为当前的节点产生合理的嵌入，而且还为新看不见的节点生成合理的嵌入。实验结果表明，所提出的Sanne在众所周知的基准数据集上获得了节点分类任务的最新结果。
Despite several signs of progress have been made recently, limited research has been conducted for an inductive setting where embeddings are required for newly unseen nodes -- a setting encountered commonly in practical applications of deep learning for graph networks. This significantly affects the performances of downstream tasks such as node classification, link prediction or community extraction. To this end, we propose SANNE -- a novel unsupervised embedding model -- whose central idea is to employ a transformer self-attention network to iteratively aggregate vector representations of nodes in random walks. Our SANNE aims to produce plausible embeddings not only for present nodes, but also for newly unseen nodes. Experimental results show that the proposed SANNE obtains state-of-the-art results for the node classification task on well-known benchmark datasets.
