Deep learning algorithms source code for beginners
关键参数与规格
关键参数与规格
参数
值
语言
Jupyter Notebook
类别
Tutorials
子类别
ML/AI fundamentals
星标数
1,236
分叉数
226
贡献者
5
创建日期
2019-08-07
更新日期
2026-10-08
1 天星标数
0
7 天星标数
0
排名
11,025
上次排名
10,982
排名变化
-43
7 天星标排名
12,855
上次 7 天星标排名
12,874
7 天星标排名变化
19
总星标排名
9,466
上次总星标排名
9,456
总星标排名变化
-10
国家
India
README 摘录
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.
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}},
}
