链接注入（Link Injection）
Beyond Observed Connections : Link Injection
摘要：
在本文中，我们提出了\textit{link injection}，这是一种新的方法，可以帮助任何可微图机器学习模型以端到端的学习方式超越从输入数据中观察到的连接。它通过参数链接注入层找出有利于当前任务的（弱）连接，这些连接不存在于输入数据中。我们使用一系列最先进的图卷积网络对节点分类和链接预测任务进行评估。结果表明，链接注入有助于各种模型，以实现更好的性能在这两个应用程序。进一步的实证分析表明，这种方法在有效地利用注入的链接看不见的连接的巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
