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Found 5 papers, 2 papers with code
Federated Graph Representation Learning using Self-Supervision
1. 7% gains compared to individual client specific self-supervised training and (2) we construct and introduce a new cross-silo dataset called Amazon Co-purchase Networks that have both the characteristics of the motivated problem setting.
OCTAL: Graph Representation Learning for LTL Model Checking
Model Checking is widely applied in verifying the correctness of complex and concurrent systems against a specification.
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
We find that the prediction performance of a wide range of GNN models is highly correlated with the node level assortativity.
Graph Learning
Node Classification on Non-Homophilic (Heterophilic) Graphs
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data.
A Hybrid Model for Learning Embeddings and Logical Rules Simultaneously from Knowledge Graphs
Our method uses a cross feedback paradigm wherein, an embedding model is used to guide the search of a rule mining system to mine rules and infer new facts.
