poses significant computational challenges for training GNNs. More dramatically, the computational cost continues to increase when we need to retrain the models multiple times, e.g., under incremental learning settings, hyperparameter and neural architecture search....
While other machine learning methods, e.g., convolutional neural networks are at the peak of publication activity, GNNs are still rising exponentially, with hundreds of papers per year since 2019. Their architecture allows them to directly work on natural input representations of molecules and ...
Pitfalls of Graph Neural Network Evaluation Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann arXiv 2018 .11 Heterogeneous Graph Attention Network Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye WWW 2019 Bayesian graph convolutional neural netw...
V. Neural architecture search with reinforcement learning. The 5th International Conference on Learning Representations (ICLR, 2017). Zhang, X. & Zitnik, M. GNNGuard: defending graph neural networks against adversarial attacks. In Advances in Neural Information Processing Systems 9263–9275 (NeurIPS, ...
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
experimental data. However, GNNs explore to generate the graph from non-structural data like scene pictures and story documents, which can be a powerful neural model for further high-level AI. Recently, it has been proved that an untrained GNN with a simple architecture also perform well [21]...
with them. In recent years, systems based on variants of graph neural networks such as graph convolutional network (GCN), graph attention network (GAT), gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a ...
To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples output...
How powerful are graph neural networks? In International Conference on Learning Representations (2019). Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet—a deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (...
Graphnas: Graph neural architecture search with reinforcement learning. arXiv 2019, arXiv:1904.09981. [Google Scholar] Zhang, J. Get rid of suspended animation problem: Deep diffusive neural network on graph semi-supervised classification. arXiv 2020, arXiv:2001.07922. [Google Scholar] Rychalska, ...