pytorch_geometric_temporal VS dgl

Compare pytorch_geometric_temporal vs dgl and see what are their differences.

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pytorch_geometric_temporal dgl
18 4
2,436 12,913
- 1.4%
2.6 9.9
10 days ago about 16 hours ago
Python Python
MIT License Apache License 2.0
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pytorch_geometric_temporal

Posts with mentions or reviews of pytorch_geometric_temporal. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-01-24.
  • Ask HN: ML Papers to Implement
    3 projects | news.ycombinator.com | 24 Jan 2023
    I have done this a few times now. Alone (e.g. https://github.com/paulmorio/geo2dr) and in collaboration with others (e.g. https://github.com/benedekrozemberczki/pytorch_geometric_tem...) primarily as a way to learn about the methods I was interested in from a research perspective whilst improving my skills in software engineering. I am still learning.

    Starting out I would recommend implementing fundamental building blocks within whatever 'subculture' of ML you are interested in whether that be DL, kernel methods, probabilistic models, etc.

    Let's say you are interested in deep learning methods (as that's something I could at least speak more confidently about). In that case build yourself an MLP layer, then an RNN layer, then a GNN layer, then a CNN layer, and an attention layer along with some full models with those layers on some case studies exhibiting different data modalities (images, graphs, signals). This should give you a feel for the assumptions driving the inductive biases in each layer and what motivates their existence (vs. an MLP). It also gives you the all the building blocks you can then extend to build every other DL layer+model out there. Another reason is that these fundamental building blocks have been implemented many times so you have a reference to look to when you get stuck.

    On that note: here are some fun GNN papers to implement in order of increasing difficulty (try building using vanilla PyTorch/Jax instead of PyG).

dgl

Posts with mentions or reviews of dgl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-03.

What are some alternatives?

When comparing pytorch_geometric_temporal and dgl you can also consider the following projects:

pytorch_geometric - Graph Neural Network Library for PyTorch

osmnx - OSMnx is a Python package to easily download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

torchdrug - A powerful and flexible machine learning platform for drug discovery

graphein - Protein Graph Library

gnn - TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations.

spektral - Graph Neural Networks with Keras and Tensorflow 2.

deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org

SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

euler - A distributed graph deep learning framework.