论文标题
路径感知的暹罗图神经网络用于链路预测
Path-aware Siamese Graph Neural Network for Link Prediction
论文作者
论文摘要
在本文中，我们提出了一个通知的暹罗图神经网络（PSG），以用于链接预测任务。首先，PSG捕获了给定两个节点的节点和边缘特征，即k-邻晶的结构信息和节点的继电器路径信息。此外，提出了一个新型的多任务GNN框架，具有自我监督的对比度学习，以区分积极的联系和负面联系，而节点的内容和行为可以同时捕获。我们在两个链接属性预测数据集（OGBL-DDI和OGBL-Collab）上评估了所提出的算法PSG。 PSG在OGBL-DDI上取得了前1位的表现，直到提交并在OGBL-Collab上表现前三名。实验结果验证了我们提出的PSG的优势
In this paper, we propose a Path-aware Siamese Graph neural network(PSG) for link prediction tasks. First, PSG captures both nodes and edge features for given two nodes, namely the structure information of k-neighborhoods and relay paths information of the nodes. Furthermore, a novel multi-task GNN framework with self-supervised contrastive learning is proposed for differentiation of positive links and negative links while content and behavior of nodes can be captured simultaneously. We evaluate the proposed algorithm PSG on two link property prediction datasets, ogbl-ddi and ogbl-collab. PSG achieves top 1 performance on ogbl-ddi until submission and top 3 performance on ogbl-collab. The experimental results verify the superiority of our proposed PSG
