Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains. The performances of current DG methods largely rely on sufficient labeled data, which are usually costly or unavailable, however. Since unlabeled data are far more accessible, we seek to explore how unsupervised learning can help deep models generalize across domains. Specifically, we study a novel generalization problem called unsupervised domain generalization (UDG), which aims to learn generalizable models with unlabeled data and analyze the effects of pre-training on DG. In UDG, models are pretrained with unlabeled data from various source domains before being trained on labeled source data and eventually tested on unseen target domains. Then we propose a method named Domain-Aware Representation LearnING (DARLING) to cope with the significant and misleading heterogeneity within unlabeled pretraining data and severe distribution shifts between source and target data. Surprisingly we observe that DARLING can not only counterbalance the scarcity of labeled data but also further strengthen the generalization ability of models when the labeled data are insufficient. As a pretraining approach, DARLING shows superior or comparable performance compared with ImageNet pretraining protocol even when the available data are unlabeled and of a vastly smaller amount compared to ImageNet, which may shed light on improving generalization with large-scale unlabeled data.

With both positive and negative links, signed graphs exist widely in the real world. Recently, signed graph neural networks (GNNs) have shown superior performance in the most common signed graph analysis task, i.e., link sign prediction. Existing signed GNNs follow the classic nonlinear-propagation paradigm in unsigned GNNs. However, several recent studies on unsigned GNNs have shown that such a paradigm increases training difficulty and even reduces performance in various unsigned graph analysis tasks. Meanwhile, most of the public real-world signed graph datasets do not provide node features. These motivate us to consider whether the existing complex model architecture is suitable.

Signed graph representation learning is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links. Most previous signed graph representation learning methods resort to balance theory, a classic social theory that originated from psychology as the core assumption. However, since balance theory is shown equivalent to a simple assumption that nodes can be divided into two conflicting groups, it fails to model the structure of real signed graphs. To solve this problem, we propose Group Signed Graph Neural Network (GS-GNN) model for signed graph representation learning beyond the balance theory assumption. GS-GNN has a dual GNN architecture that consists of the global and the local module. In the global module, we adopt a more generalized assumption that nodes can be divided into multiple latent groups and that the groups can have arbitrary relations and propose a novel prototype-based GNN to learn node representations based on the assumption. In the local module, to give the model enough flexibility in modeling other factors, we do not make any prior assumptions, treat positive links and negative links as two independent relations, and adopt a relational GNN to learn node representations. Both modules can complement each other, and the concatenation of two modules is fed into downstream tasks. Extensive experimental results demonstrate the effectiveness of our GS-GNN model on both synthetic and real-world signed graphs by greatly and consistently outperforming all the baselines and achieving new state-of-the-art results. Our implementation is available in PyTorch.