Tag: graph learning
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Enhancing Graph Few-Shot Learning with Hyperbolic Space
Discover how hyperbolic space and denoising diffusion improve graph few-shot learning for better accuracy with limited data.
Robust Learning on Heterogeneous Graphs with HGUL Framework
Discover the HGUL framework for robust learning on heterogeneous graphs with heterophily, tackling structural noise for improved graph representation.
Hysteresis Graph ODEs for Dynamic Topology-Feature Modeling
Discover Hysteresis Graph ODEs (HGODE) that improve graph neural networks by modeling coupled topology and feature evolution via continuous phase transitio...
TACENR: Explainable Task-Agnostic Node Representations
Discover TACENR, a task-agnostic method offering contrastive explanations for node representations to enhance transparency in graph learning.
CD-GNN: Causal Learning for Heterophilic Graphs
Discover how CD-GNN leverages causal disentanglement to improve accuracy in heterophilic graph learning by addressing spurious inductive subgraphs.