Semi-supervised graph learning aims to improve learning performance by leveraging unlabeled nodes. Typically, it can be approached in two different ways, includingpredictive representation learning(PRL) where unlabeled data provide clues on input distribution andlabel-dependent regularization(LDR) which smooths the output distribution with unlabeled nodes to improve generalization. However, most existing PRL approaches suffer from overfitting in an end-to-end setting or cannot encode task-specific information when used as unsupervised pre-training (i.e., two-stage learning). Meanwhile, LDR strategies often introduce redundant and invalid data perturbations that can slow down and mislead the training. To address all these issues, we propose a general framework SemiGraL for semi-supervised learning on graphs, which bridges and facilitates both PRL and LDR in a single shot. By extending a contrastive learning architecture to the semi-supervised setting, we first develop asemi-supervised contrastive representation learningprocess with virtual adversarial augmentation to map input nodes into a label-preserving representation space while avoiding overfitting. We then introduce amultiview consistency classificationprocess with well-constrained perturbations to achieve adversarially robust classification. Extensive experiments on seven semi-supervised node classification benchmark datasets show that SemiGraL outperforms various baselines while enjoying strong generalization and robustness performance.

Community detection is one of the fundamental tasks in graph mining, which aims to identify group assignment of nodes in a complex network. Recently, network embedding techniques have demonstrated their strong power in advancing the community detection task and achieve better performance than various traditional methods. Despite their empirical success, most of the existing algorithms directly leverage the observed coarse network structure for community detection. Therefore, they often lead to suboptimal performance as the observed connections fail to capture the essential tie strength information among nodes precisely and account for the impact of noisy links. In this paper, an optimal network structure for community detection is introduced to characterize the fine-grained tie strength information between connected nodes and alleviate the adverse effects of noisy links. To obtain an expressive node representation for community detection, we learn the optimal network structure and network embeddings in a joint framework, instead of using a two-stage approach to derive the node embeddings from the coarse network topology. In particular, we formulate the joint framework as an optimization problem and an alternating optimization algorithm is exploited to solve the proposed optimization problem. Additionally, theoretical analyses regarding the computational complexity and the convergence of the optimization algorithm are also provided. Extensive experiments on both synthetic and real-world networks demonstrate the effectiveness and superiority of the proposed framework.