本文将解读蚂蚁集团图学习团队在ECML PKDD 2021上发表的论文《Inductive Link Prediction with Interactive Structure Learning on Attributed Graph》。该论文提出的路径感知图神经网络PaGNN主要探索了在链接预测任务中如何将节点间的复杂网络结构学习集成到传统GNN的计算范式中，从而提高链接预测的准确率。目前，PaGNN已成功应用于蚂蚁集团多个数字金融业务场景中。
Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on manual labor. Thus, automated graph neural network (AutoGNN) has attracted interest and attention from the research community, which makes significant performance improvements in recent years. However, existing AutoGNN works mainly adopt an implicit way to model and leverage the link information in the graphs, which is not well regularized to the link prediction task on graphs, and limits the performance of AutoGNN for other graph tasks. In this paper, we present a novel AutoGNN work that explicitly models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can handle the link prediction task and improve the performance of AutoGNNs on the node classification and graph classification task. Specifically, AutoGEL proposes a novel search space containing various design dimensions at both intra-layer and inter-layer designs and adopts a more robust differentiable search algorithm to further improve efficiency and effectiveness. Experimental results on benchmark data sets demonstrate the superiority of AutoGEL on several tasks.