https://github.com/benedekrozemberczki/awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.
https://github.com/benedekrozemberczki/awesome-graph-classification

List: awesome-graph-classification

attention-mechanism classification-algorithm deep-graph-kernels deepwalk graph-attention-model graph-attention-networks graph-classification graph-convolutional-networks graph-embedding graph-kernel graph-kernels graph-representation-learning graph2vec kernel-methods netlsd network-embedding node-embedding node2vec structural-attention weisfeiler-lehman

Last synced: 1 day ago
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A collection of important graph embedding, classification and representation learning papers with implementations.

• Host: GitHub
• URL: https://github.com/benedekrozemberczki/awesome-graph-classification
• Owner: benedekrozemberczki
• License: cc0-1.0
• Created: 2018-07-14T12:13:44.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-18T12:10:08.000Z (almost 2 years ago)
• Last Synced: 2024-10-29T15:33:40.477Z (about 2 months ago)
• Topics: attention-mechanism, classification-algorithm, deep-graph-kernels, deepwalk, graph-attention-model, graph-attention-networks, graph-classification, graph-convolutional-networks, graph-embedding, graph-kernel, graph-kernels, graph-representation-learning, graph2vec, kernel-methods, netlsd, network-embedding, node-embedding, node2vec, structural-attention, weisfeiler-lehman
• Language: Python
• Homepage:
• Size: 1.79 MB
• Stars: 4,754
• Watchers: 217
• Forks: 742
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Contributing: contributing.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE
  • Code of conduct: code-of-conduct.md

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README

        
# Awesome Graph Classification

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

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)

![License](https://img.shields.io/github/license/benedekrozemberczki/awesome-graph-embedding.svg?color=blue)

[![repo size](https://img.shields.io/github/repo-size/benedekrozemberczki/awesome-graph-classification.svg)](https://github.com/benedekrozemberczki/awesome-graph-classification/archive/master.zip) [![benedekrozemberczki](https://img.shields.io/twitter/follow/benrozemberczki?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=benrozemberczki) 

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.

Relevant graph classification benchmark datasets are available [[here]](https://github.com/shiruipan/graph_datasets).

Similar collections about [community detection](https://github.com/benedekrozemberczki/awesome-community-detection), [classification/regression tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers), [fraud detection](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers), [Monte Carlo tree search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers), and [gradient boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) papers with implementations.



  



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## Contents  

1. [Matrix Factorization](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/matrix_factorization.md)  

2. [Spectral and Statistical Fingerprints](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/fingerprints.md)

3. [Deep Learning](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/deep_learning.md)  

4. [Graph Kernels](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/chapters/kernels.md)

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**License**

- [CC0 Universal](https://github.com/benedekrozemberczki/awesome-graph-classification/blob/master/LICENSE)