Network embedding is aimed at mapping nodes in a network into low-dimensional vector representations. Graph Neural Networks (GNNs) have received widespread attention and lead to state-of-the-art performance in learning node representations. However, most GNNs only work in unsigned networks, where only positive links exist. It is not trivial to transfer these models to signed directed networks, which are widely observed in the real world yet less studied. In this paper, we first review two fundamental sociological theories (i.e., status theory and balance theory) and conduct empirical studies on real-world datasets to analyze the social mechanism in signed directed networks. Guided by related sociological theories, we propose a novel Signed Directed Graph Neural Networks model named SDGNN to learn node embeddings for signed directed networks. The proposed model simultaneously reconstructs link signs, link directions, and signed directed triangles. We validate our model's effectiveness on five real-world datasets, which are commonly used as the benchmark for signed network embedding. Experiments demonstrate the proposed model outperforms existing models, including feature-based methods, network embedding methods, and several GNN methods.

翻译：网络嵌入旨在将网络中的节点映射为低维向量表示。图神经网络（GNNs）在节点表示学习方面受到广泛关注并取得了最先进的性能。然而，大多数GNNs仅适用于无符号网络（仅存在正链接）。将这些模型迁移到现实世界中广泛存在但研究较少的带符号有向网络并非易事。本文首先回顾了两种基础社会学理论（即地位理论和平衡理论），并在真实数据集上进行实证研究，以分析带符号有向网络中的社会机制。基于相关社会学理论的指导，我们提出了一种名为SDGNN的新型带符号有向图神经网络模型，用于学习带符号有向网络的节点嵌入。该模型同时重建链接符号、链接方向以及带符号有向三角形。我们在五个常用作带符号网络嵌入基准的真实数据集上验证了模型的有效性。实验表明，所提模型在性能上优于现有方法，包括基于特征的方法、网络嵌入方法以及多种GNN方法。