PyTorch Geometric is an extension library for PyTorch that makes it possible to perform usual deep learning tasks on non-euclidean data. The "Geometric" in its name is a reference to the definition for the field coined by Bronstein et al. 4 4 3 3 Why is …
When comparing pytorch_geometric and dgl you can also consider the following projects: pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021) torchdrug - A powerful and flexible machine learning platform for drug discovery.
Jun 23, 2020 · In terms of performance, they are also similar (some GNNs are a bit faster on PyG and some are a bit slower). However, DGL has a better memory management for GNNs that can be expressed as a sparse matrix multiplication, but PyG will soon catch up.
Jul 09, 2019 · I agree that dgl has better design, but pytorch geometric has reimplementations of most of the known graph convolution layers and pooling available for use off the shelf. I think that’s a big plus if I’m just trying to test out a few GNNs on a dataset to see if it works.
dgl VS pytorch_geometric Compare dgl vs pytorch_geometric and see what are their differences. dgl. Python package built to ease deep learning on graph, on top of existing DL frameworks. (by dmlc) #Deep Learning #graph-neural-networks. Source Code. dgl.ai. pytorch_geometric.
23.06.2020 · DGL has great sampling support. PyG recently also added better support for sampling via NeighborSampler, GraphSAINT and ClusterGCN. In terms of data handling, it boils down to the question whether you like networkx or not. DGL has a similar graph interface to networkx, where as PyG provides all data as pure PyTorch tensors. Author
PyTorch Geometric vs DGL? Close. 2. Posted by 1 year ago. Archived. PyTorch Geometric vs DGL? Hi, I'm new to graph neural networks and I'm finding tools for ...
13 人 赞同了该回答. DGL 和 PyG 都支持 PyTorch ，这两个库各有优点，其实如果熟悉了图神经网络的基本原理，这两个库上手都很快，而且他们也都提供了很多实现好的例子。. 引用 DGL 的作者的话. Overall, I think both frameworks have their merits. PyG is very light-weighted and has lots ...
PyTorch Geometric vs DGL? Hi, I'm new to graph neural networks and I'm finding tools for implementing them. I found two packages: PyTorch Geometric and DGL. I wonder what are the pros and cons for each, or which one you are using or would recommend? Thanks. 1 comment. share. save. hide.
When comparing pytorch_geometric and dgl you can also consider the following projects: pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021) torchdrug - A powerful and flexible machine learning platform for drug discovery.
17.08.2021 · I’m new to PyTorch-geometric and geometric deep learning. I am going through the implementation of the graph convolution network implemented in both Pytorch geometric and Deep-Graph-Libray. But it seems to me both the im…
09.07.2019 · I agree that dgl has better design, but pytorch geometric has reimplementations of most of the known graph convolution layers and pooling available for use off the shelf. I think that’s a big plus if I’m just trying to test out a few GNNs on a dataset to see if it works. zcwang0702 July 10, 2019, 5:08pm #5
Casual hobbyist: If you're interested in testing Graph Neural Networks, no strings attached, the fastest way possible, then there's no beating PyTorch Geometric. The sheer amount of example implementations you can have a look and adjust is astounding. DGL is a close second, necessitating a higher time investment to get going.