论文标题
超越观察到的连接：链接注入
Beyond Observed Connections : Link Injection
论文作者
论文摘要
在本文中，我们提出了\ textit {link注入}，这是一种新颖的方法，可帮助任何可区分的图形机器学习模型以端到端学习方式超越从输入数据观察到的连接。它发现（弱）连接，有利于通过参数链接注入层中输入数据中不存在的当前任务。我们使用一系列最先进的图形卷积网络对节点分类和链接预测任务进行评估。结果表明，链路注入有助于各种模型在这两种应用程序上都能取得更好的性能。进一步的经验分析表明，这种方法在有效利用注射链接的未见连接方面具有巨大的潜力。
In this paper, we proposed the \textit{link injection}, a novel method that helps any differentiable graph machine learning models to go beyond observed connections from the input data in an end-to-end learning fashion. It finds out (weak) connections in favor of the current task that is not present in the input data via a parametric link injection layer. We evaluate our method on both node classification and link prediction tasks using a series of state-of-the-art graph convolution networks. Results show that the link injection helps a variety of models to achieve better performances on both applications. Further empirical analysis shows a great potential of this method in efficiently exploiting unseen connections from the injected links.
