论文标题
通过图神经网络进行的几个链接链接预测，用于covid-19
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
论文作者
论文摘要
预测异质图结构化数据之间的相互作用具有许多应用，例如知识图完成，建议系统和药物发现。通常，要预测的链接属于稀有类型，例如重新利用新型疾病的药物的情况。这激发了几个链接预测的任务。通常，GCN在学习这种罕见的链接类型方面缺乏障碍，因为没有以归纳方式学习关系嵌入。本文提出了一个归纳性RGCN，用于学习信息性关系嵌入，即使在少数学习制度中也是如此。提出的归纳模型在几乎没有射门的学习任务中显着优于RGCN和最先进的KGE模型。此外，我们将我们的方法应用于药物替代知识图（DRKG），以发现Covid-19的药物。我们将药物发现任务作为链接预测提出，并为参与DRKG的生物实体学习嵌入。我们的最初结果证实了临床试验中使用的几种药物被鉴定为可能的候选药物。本文中的方法是使用有效的深图学习（DGL）实现的
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to rare types such as the case in repurposing drugs for novel diseases. This motivates the task of few-shot link prediction. Typically, GCNs are ill-equipped in learning such rare link types since the relation embedding is not learned in an inductive fashion. This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime. The proposed inductive model significantly outperforms the RGCN and state-of-the-art KGE models in few-shot learning tasks. Furthermore, we apply our method on the drug-repurposing knowledge graph (DRKG) for discovering drugs for Covid-19. We pose the drug discovery task as link prediction and learn embeddings for the biological entities that partake in the DRKG. Our initial results corroborate that several drugs used in clinical trials were identified as possible drug candidates. The method in this paper are implemented using the efficient deep graph learning (DGL)
