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Found 9 papers, 6 papers with code
Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks
Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.
Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective
Our empirical results reveal that our proposed size-insensitive attention strategy substantially enhances graph classification performance on large test graphs, which are 2-10 times larger than the training graphs, resulting in an improvement in F1 scores by up to 8%.
A Dataset-Dispersion Perspective on Reconstruction Versus Recognition in Single-View 3D Reconstruction Networks
Our finding shows that a leading factor in determining recognition versus reconstruction is how dispersed the training data is.
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices
Unsupervised graph representation learning is critical to a wide range of applications where labels may be scarce or expensive to procure.
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
We are the first to take a unified perspective to jointly explain the oversmoothing and heterophily problems at the node level.
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
Replacing BN with the recently-proposed Group Normalization (GN) can reduce gradient diversity and improve test accuracy.
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i. e., in networks where connected nodes may have different class labels and dissimilar features.
Neural Execution Engines: Learning to Execute Subroutines
A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.
NEURAL EXECUTION ENGINES
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.
