Paper list Name Method Year Code Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation GCL+RC SIGIR '22 github Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation Fed+GNN+RC CIKM '22 Null FedEgo: Privacy-preserving Personalized Federated Graph Learning with Ego-graphs Fed+GNN arxiv 22 github Graph Contrastive Learning with Augmentations GCL NeurIPS '20 github Federated Graph Contrastive Learning Fed+GCL arxiv 22 Null Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective Privacy+GNN KDD '22 Null Federated knowledge graph completion via embedding-contrastive learning Fed+GNN+CL Knowledge-Based Systems 22 Null Towards Private Learning on Decentralized Graphs with Local Differential Privacy Privacy+GNN arxiv 22 Null Dual-Contrastive for Federated Social Recommendation CL+RC IJCNN 22 Null Quantifying and Mitigating Privacy Risks of Contrastive Learning Privacy+CL CCS '21 Null SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data Fed+GNN AAAI '22 Null FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation Fed+CL arxiv 22 Null Subgraph Federated Learning with Missing Neighbor Generation Fed+GNN NeurIPS '21 Null Fast-adapting and privacy-preserving federated recommender system Fed+RC The VLDB Journal Null