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Found 5 papers, 3 papers with code
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive computations.
SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i. e. neighboring nodes are dissimilar, due to their local and uniform aggregation.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Recent advances in data processing have stimulated the demand for learning graphs of very large scales.
A Survey on Machine Learning Solutions for Graph Pattern Extraction
Many algorithms are proposed in studying subgraph problems, where one common approach is by extracting the patterns and structures of a given graph.
Achieving Adversarial Robustness via Sparsity
Through experiments on a variety of adversarial pruning methods, we find that weights sparsity will not hurt but improve robustness, where both weights inheritance from the lottery ticket and adversarial training improve model robustness in network pruning.
