pytorch_geometric_temporal VS Graph-Convolution-on-Structured-Documents

Compare pytorch_geometric_temporal vs Graph-Convolution-on-Structured-Documents and see what are their differences.

Graph-Convolution-on-Structured-Documents

This repo contains code to convert Structured Documents to Graphs and implement a Graph Convolution Neural Network for node classification (by dhavalpotdar)
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pytorch_geometric_temporal Graph-Convolution-on-Structured-Documents
18 1
2,436 141
- -
2.6 0.0
10 days ago over 1 year ago
Python Python
MIT License -
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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).

Graph-Convolution-on-Structured-Documents

Posts with mentions or reviews of Graph-Convolution-on-Structured-Documents. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning Graph-Convolution-on-Structured-Documents yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing pytorch_geometric_temporal and Graph-Convolution-on-Structured-Documents you can also consider the following projects:

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

dgl - Python package built to ease deep learning on graph, on top of existing DL frameworks.

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

graphein - Protein Graph Library

pytorch_geometric - Graph Neural Network Library for PyTorch

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

euler - A distributed graph deep learning framework.

karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

RecBole - A unified, comprehensive and efficient recommendation library

PDN - The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

awesome-drug-pair-scoring - Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)