Deep learning algorithms source code for beginners.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Jupyter Notebook
Category
Tutorials
Subcategory
ML/AI fundamentals
Stars
1,236
Forks
226
Contributors
5
Created date
2019-08-07
Updated date
2026-10-08
Stars in 1 day
0
Stars in 7 days
0
Rank
11,025
Previous rank
10,982
Rank change
-43
7-day stars rank
12,855
Previous 7-day stars rank
12,874
7-day stars rank change
19
Total stars rank
9,466
Previous total stars rank
9,456
Total stars rank change
-10
Country
India
README summary
Graph Representation Learning This repo is a supplement to our blog series *Explained: Graph Representation Learning*. The following major papers and corresponding blogs have been covered as part of the series and we look to add blogs on a few other significant works in the field. Setup Clone the git repository : Python 3 with Pytorch 1. The file contains a listing of other dependencies. To install all the requirements, run the following: 1. Understanding DeepWalk Unsupervised online learning approach, inspired
from word2vec in NLP, but, here the goal is to generate node embeddings. DeepWalk Blog Jupyter Notebook Code Paper -> DeepWalk: Online Learning of Social Representations 2. A Review : Graph Convolutional Networks (GCN) GCNs draw on the idea of Convolution Neural Networks re-defining them for the non-euclidean data domain. They are convolutional, because filter parameters are typically shared over all locations in the graph unlike typical GNNs. GCN Blog Jupyter Notebook Code Paper -> Semi-Supervised Classification with Graph Convolutional
Networks 3. Graph SAGE(SAmple and aggreGatE) Previous approaches are transductive and don't naturally generalize to unseen nodes. GraphSAGE is an inductive framework leveraging node feature information to efficiently generate node embeddings. GraphSAGE Blog Jupyter Notebook Code Paper -> Inductive Representation Learning on Large Graphs 4. ChebNet: CNN on Graphs with Fast Localized Spectral Filtering ChebNet is a formulation of CNNs in the context of spectral graph theory. ChebNet Blog Jupyter Notebook Code Paper -> Convolutional Neural Networks on Graphs with Fast
Localized Spectral Filtering 5. Understanding Graph Attention Networks GAT is able to attend over their neighborhoods’ features, implicitly specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation or depending on knowing the graph structure upfront. GAT Blog Jupyter Notebook Code Paper -> Graph Attention Networks Citation Please use the following entry for citing the blog.
Frequently asked questions
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dsgiitr/graph_nets has 1,236 stars and 226 forks.
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dsgiitr/graph_nets is ranked 11,025.
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dsgiitr/graph_nets has 1,236 stars, uses Jupyter Notebook and belongs to Tutorials.
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dsgiitr/graph_nets is compared with campusx-official/ML-Roadmap-for-2022, ranked 11,030 with 1,235 stars.
README
Graph Representation Learning
This repo is a supplement to our blog series Explained: Graph Representation Learning. The following major papers and corresponding blogs have been covered as part of the series and we look to add blogs on a few other significant works in the field.
Python 3 with Pytorch 1.3.0 are the primary requirements. The requirements.txt file contains a listing of other dependencies. To install all the requirements, run the following:
pip install -r requirements.txt
Understanding DeepWalk
Unsupervised online learning approach, inspired from word2vec in NLP, but, here the goal is to generate node embeddings.
GCNs draw on the idea of Convolution Neural Networks re-defining them for the non-euclidean data domain. They are convolutional, because filter parameters are typically shared over all locations in the graph unlike typical GNNs.
Previous approaches are transductive and don't naturally generalize to unseen nodes. GraphSAGE is an inductive framework leveraging node feature information to efficiently generate node embeddings.
GAT is able to attend over their neighborhoods’ features, implicitly specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation or depending on knowing the graph structure upfront.
Please use the following entry for citing the blog.
@misc{graph_nets,
author = {A. Dagar and A. Pant and S. Gupta and S. Chandel},
title = {graph_nets},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/dsgiitr/graph_nets}},
}
