Self-supervised studying (SSL) on graphs generates node and graph representations (i.e., embeddings) that can be utilized for downstream duties equivalent to node classification, node clustering, and hyperlink prediction. Graph SSL is especially helpful in eventualities with restricted or no labeled information. Present SSL strategies predominantly observe contrastive or generative paradigms, every excelling in several duties: contrastive strategies usually carry out nicely on classification duties, whereas generative strategies usually excel in hyperlink prediction. On this paper, we current a novel structure for graph SSL that integrates the strengths of each approaches. Our framework introduces community-aware node-level contrastive studying, offering extra sturdy and efficient constructive and unfavorable node pairs era, alongside graph-level contrastive studying to seize international semantic info. Moreover, we make use of a complete augmentation technique that mixes function masking, node perturbation, and edge perturbation, enabling sturdy and various illustration studying. By incorporating these enhancements, our mannequin achieves superior efficiency throughout a number of duties, together with node classification, clustering, and hyperlink prediction. Evaluations on open benchmark datasets exhibit that our mannequin outperforms state-of-the-art strategies, reaching a efficiency elevate of 0.23%-2.01% relying on the duty and dataset.
