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Found 6 papers, 4 papers with code
One for All: Towards Training One Graph Model for All Classification Tasks
For in-context learning on graphs, OFA introduces a novel graph prompting paradigm that appends prompting substructures to the input graph, which enables it to address varied tasks without fine-tuning.
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
In our study, we first identify two crucial advantages of contrastive learning compared to meta learning, including (1) the comprehensive utilization of graph nodes and (2) the power of graph augmentations.
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k, t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem.
Ranked #2 on
Graph Regression
on ZINC-500k
Time Associated Meta Learning for Clinical Prediction
We view time-associated disease prediction as classification tasks at multiple time points.
A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks
Learning to represent free text is a core task in many clinical machine learning (ML) applications, as clinical text contains observations and plans not otherwise available for inference.
Geodesic Graph Neural Network for Efficient Graph Representation Learning
The GNN embeddings of nodes on the shortest paths are used to generate geodesic representations.
