Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining tasks. Although real-world signed relationships can be influenced by multiple latent factors, most existing methods often oversimplify the modeling of signed relationships by relying on social theories and treating them as simplistic factors. This limits their expressiveness and their ability to capture the diverse factors that shape these relationships. In this paper, we propose DINES, a novel method for learning disentangled node representations in signed directed graphs without social assumptions. We adopt a disentangled framework that separates each embedding into distinct factors, allowing for capturing multiple latent factors. We also explore lightweight graph convolutions that focus solely on sign and direction, without depending on social theories. Additionally, we propose a decoder that effectively classifies an edge's sign by considering correlations between the factors. To further enhance disentanglement, we jointly train a self-supervised factor discriminator with our encoder and decoder. Throughout extensive experiments on real-world signed directed graphs, we show that DINES effectively learns disentangled node representations, and significantly outperforms its competitors in the sign prediction task.

翻译：有符号图是表示各种领域中信任关系或偏好的复杂系统。在此类图中学习节点表示对许多挖掘任务至关重要。尽管现实世界的有符号关系可能受多个潜在因素影响，但大多数现有方法常依赖社会理论将其简化为单一因素，从而过度简化了有符号关系的建模。这限制了这些方法的表达能力及其捕捉塑造这些关系的多样化因素的能力。本文提出DINES，一种无需社会假设即可在有符号有向图中学习解耦节点表示的新方法。我们采用解耦框架，将每个嵌入分离为不同因素，从而能够捕捉多个潜在因素。我们还探索了仅关注符号和方向的轻量级图卷积，无需依赖社会理论。此外，我们提出一种解码器，通过考虑因素之间的相关性有效分类边的符号。为进一步增强解耦性，我们在编码器和解码器的基础上联合训练一个自监督因素判别器。通过在真实世界有符号有向图上的大量实验，我们证明DINES能有效学习解耦节点表示，并在符号预测任务中显著优于其竞争对手。