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Found 4 papers, 3 papers with code
Gradient Gating for Deep Multi-Rate Learning on Graphs
We present Gradient Gating (G$^2$), a novel framework for improving the performance of Graph Neural Networks (GNNs).
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Understanding convolution on graphs via energies
We do so by showing that linear graph convolutions with symmetric weights minimize a multi-particle energy that generalizes the Dirichlet energy; in this setting, the weight matrices induce edge-wise attraction (repulsion) through their positive (negative) eigenvalues, thereby controlling whether the features are being smoothed or sharpened.
Graph-Coupled Oscillator Networks
This demonstrates that the proposed framework mitigates the oversmoothing problem.
Tuning Word2vec for Large Scale Recommendation Systems
We show that applying constrained hy-perparameter optimization using only a 10% sample of the data still yields a 91%average improvement in hit rate over the default parameters when applied to thefull datasets.
