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....
Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in low solvus high-re
Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all ...
Boyang Ding, Quan Wang, Bin Wang, Li Guo ACL 2018 SimplE Embedding for Link Prediction in Knowledge Graphs Seyed Mehran Kazemi, David Poole NeurIPS 2018 A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network Dai Quoc Nguyen, Tu Dinh Nguyen, Dat Quoc Nguyen, ...
GNN: graph neural network Contributed by Jie Zhou, Ganqu Cui, Zhengyan Zhang and Yushi Bai. Content 1. Survey 2. Models 2.1 Basic Models 2.2 Graph Types 2.3 Pooling Methods 2.4 Analysis 2.5 Efficiency 3. Applications 3.1 Physics 3.2 Chemistry and Biology 3.3 ...
Graph Neural Networks: A Review of Methods and Applications A Comprehensive Survey on Graph Neural Networks 主题:图神经网络(Graph neural networks)综述 整合作者:Reddoge 1 引言 近年来,人工智能领域在科研领域取得了巨大的成功,影响到了人们生活的方方面面,其中,深度学习(Deep learning),作为机器学习的一分子...
Graph Neural Networks LabML. https://nn.labml.ai/graphs/index.html (2023).7.LaBonne, M. Graph Attention Networks: Theoretical and Practical Insights https : / / mlabonne . github.io/blog/posts/2022-03-09-graph_attention_net...
通用图生成GraphRNN《GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models》 通用图生成MolGAN《 Molgan: An implicit generative model for small molecular graphs》 决策优化旅行商问题GNN《Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP》《Attention solves your...
Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed ...
B. MolCLR: molecular contrastive learning of representations via graph neural networks. CodeOcean https://doi.org/10.24433/CO.8582800.v1 (2021). Chen, T., Kornblith, S., Swersky, K., Norouzi, M. & Hinton, G. Big self-supervised models are strong semi-supervised learners. Preprint at ...