标题： 面向边缘中心应用的混合监督与自监督图神经网络 显示英文标题

标题： A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications

摘要： 本文提出了一种基于图的深度学习模型，用于涉及两个节点之间关系的任务（边缘中心任务），其重点在于预测节点对之间的关系和交互，而不是节点本身的属性。 该模型结合了监督学习和自监督学习，在损失函数中考虑了学习到的嵌入以及有和没有真实标签的模式。 此外，它还融合了一个注意力机制，利用节点和边的特征。 该架构是端到端训练的，包括两个主要组件：嵌入生成和预测。 首先，图神经网络（GNN）将原始节点特征转换为密集的、低维的嵌入，并结合边属性。 然后，一个前馈神经网络处理节点嵌入以生成最终输出。 实验表明，我们的模型在蛋白质-蛋白质相互作用预测和基因本体（GO）术语预测方面与现有方法相当或更优。 该模型在节点特征使用独热编码时也表现良好，为之前未解决的预测结构未知化合物之间相似性的问题提供了解决方案。 显示英文摘要

摘要： This paper presents a novel graph-based deep learning model for tasks involving relations between two nodes (edge-centric tasks), where the focus lies on predicting relationships and interactions between pairs of nodes rather than node properties themselves. This model combines supervised and self-supervised learning, taking into account for the loss function the embeddings learned and patterns with and without ground truth. Additionally it incorporates an attention mechanism that leverages both node and edge features. The architecture, trained end-to-end, comprises two primary components: embedding generation and prediction. First, a graph neural network (GNN) transform raw node features into dense, low-dimensional embeddings, incorporating edge attributes. Then, a feedforward neural model processes the node embeddings to produce the final output. Experiments demonstrate that our model matches or exceeds existing methods for protein-protein interactions prediction and Gene Ontology (GO) terms prediction. The model also performs effectively with one-hot encoding for node features, providing a solution for the previously unsolved problem of predicting similarity between compounds with unknown structures.