https://github.com/tonysy/awesome-graph-networks

Materials for Graph Models and Graph Networks
https://github.com/tonysy/awesome-graph-networks

List: awesome-graph-networks

artificial-intelligence deep-learning probabilistic-graphical-models

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Materials for Graph Models and Graph Networks

• Host: GitHub
• URL: https://github.com/tonysy/awesome-graph-networks
• Owner: tonysy
• Created: 2018-06-30T12:14:33.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-06T15:22:23.000Z (over 6 years ago)
• Last Synced: 2024-05-21T08:33:52.380Z (7 months ago)
• Topics: artificial-intelligence, deep-learning, probabilistic-graphical-models
• Homepage:
• Size: 23.7 MB
• Stars: 10
• Watchers: 4
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-of-awesome-ml - awesome-graph-networks (by tonysy)

README

        
# Awesome-Graph-Networks

[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)

Materials for Graph Models and Graph Networks

## 1. Probabilistic Graph Models

- [(Stanford)CS 228: Probabilistic Graphical Models](https://cs.stanford.edu/~ermon/cs228/index.html)

    - [[Slides]](https://ermongroup.github.io/cs228-notes/)

    - [[Videos]](https://www.youtube.com/playlist?list=PLBAGcD3siRDjiQ5VZQ8t0C7jkHQ8fhuq8)

- [(Coursera)Probabilistic Graphical Models](https://www.coursera.org/learn/probabilistic-graphical-models/)

    - [[Slides]](./Course/coursera_probabilistic_graphical_models/slides)

- [(CMU)Probabilistic Graphical Models](http://www.cs.cmu.edu/~epxing/Class/10708/lecture.html)

    - Slides are in course homepage

    - [[Videos]](https://www.youtube.com/playlist?list=PLI3nIOD-p5aoXrOzTd1P6CcLavu9rNtC-)

## 2. Graph Neural Networks

### (1) Blogs

- [GRAPH CONVOLUTIONAL NETWORKS](http://tkipf.github.io/graph-convolutional-networks/)

A good blog to introduce graph covolutional network

### (2) Papers

- [Relational inductive biases, deep learning, and graph networks(recommend to read first)](https://arxiv.org/pdf/1806.01261.pdf) 

- [Semi-Supervised Classification with Graph Convolutional Networks(ICLR 2017)](http://arxiv.org/abs/1609.02907)

- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (NIPS 2016)](https://arxiv.org/abs/1606.09375)

- [Graph Attention Networks (ICLR 2018)](https://arxiv.org/pdf/1710.10903.pdf)

- [Few-shot Learning with Graph Neural Networks (ICLR 2018)](https://arxiv.org/pdf/1711.04043.pdf)

## 3. Comments

### (1) Spectral Approach

Which works with a spectral representations of the graph and have been successfully applied in the context of node classification.

### (2) Non-spectral Approach

Which define convolutions directly on the graph, operating on groups of spatially close neighbors.