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Found 4 papers, 3 papers with code
Hyperbolic Geometric Latent Diffusion Model for Graph Generation
Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation.
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincar\'e model in hyperbolic space.
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology.
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