This record contains the dataset and codes to reproduce the published paper. Please refer to the live GitHub repository (https://github.com/rootlu/L2P-GNN) for more information. A copy of the GitHub r

DataCite Commons2022-10-25 更新100

GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts

几何深度学习基准

图神经网络

The following contains the datasets described in the paper: GDL-DS: A Benchmark for Geometric Deep Learning under Distribution Shifts, and the associated code can be found at https://github.com/Graph-

Zenodo2023-11-06 更新50

StructureSAT

图神经网络

SAT布尔可满足性问题

StructureSAT是一个大规模的饱和问题（SAT）数据集，由新南威尔士大学计算机科学与工程学院创建。该数据集包含来自不同问题领域的多样化SAT问题，旨在研究图神经网络（GNN）在SAT问题上的泛化能力。数据集涵盖了随机、设计、伪工业和工业等11个SAT领域，并研究了9种对传统SAT求解器有影响的图形结构属性。数据集通过结构属性对问题领域进行细分，以研究不同结构属性对GNN求解器泛化能力的影响

arXiv2025-02-17 更新110

Neural Force Field Model Checkpoints

计算化学力场

图神经网络

Graph neural network model checkpoints accompanying the paper "Neural Scaling of Deep Chemical Models"

Figshare2022-05-13 更新60