The recent advancements in graph-based machine learning have significantly focused on enhancing clustering and representation learning techniques. A notable trend is the integration of feature-rich approaches with graph neural networks (GNNs), addressing the limitations of traditional methods that often overlook node feature information. Innovations such as personalized and multi-granularity clustering are emerging, offering more nuanced and user-specific solutions. Additionally, contrastive learning on dynamic graphs is gaining traction, providing robust unsupervised learning mechanisms that adapt to evolving graph structures. These developments collectively push the boundaries of graph-based learning, enabling more accurate and efficient solutions for complex tasks such as link prediction, node classification, and friend suggestion in social networks. Notably, methods like Augmentation-Free Edge Contrastive Learning and Multi-view Granular-ball Contrastive Clustering stand out for their novel approaches to enhancing model performance without the need for extensive data augmentation or complex computations.