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