wsGAT：用于链接预测的加权符号图注意网络
wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
摘要：
图神经网络（GNN）已被广泛用于学习图上的表示并解决来自广泛领域的许多现实问题。在本文中，我们提出了wsGAT，这是图注意力网络（GAT）层的扩展，旨在解决缺乏可以处理具有签名和加权链接的图的GNN的问题，这些链接在信任和相关网络中无处不在。我们首先评估我们的建议的性能进行比较，对GCNII在加权链接预测任务，对SGCN在链接符号预测任务。之后，我们将这两个任务结合起来，并展示了它们在预测链接的符号权重及其存在性方面的表现。我们在真实网络上的结果表明，具有wsGAT层的模型优于具有GCNII和SGCN层的模型，并且在预测有符号权重时，性能没有损失。
英文摘要：
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention Network (GAT) layers, meant to address the lack of GNNs that can handle graphs with signed and weighted links, which are ubiquitous, for instance, in trust and correlation networks. We first evaluate the performance of our proposal by comparing against GCNII in the weighed link prediction task, and against SGCN in the link sign prediction task. After that, we combine the two tasks and show their performance on predicting the signed weight of links, and their existence. Our results on real-world networks show that models with wsGAT layers outperform the ones with GCNII and SGCN layers, and that there is no loss in performance when signed weights are predicted.
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。
