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Found 4 papers, 2 papers with code
Improving out-of-distribution generalization in graphs via hierarchical semantic environments
Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts.
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning
Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution.
SPGP: Structure Prototype Guided Graph Pooling
In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation.
Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification
To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification.
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