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https://github.com/KaiyuanZh/awesome-graph-representation-learning
A curated list of awesome graph representation learning.
https://github.com/KaiyuanZh/awesome-graph-representation-learning
List: awesome-graph-representation-learning
adversarial-learning awesome-list graph graph-convolutional-networks graph-embedding graph-mining graph-neural-networks graph-representation-learning
Last synced: about 1 month ago
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A curated list of awesome graph representation learning.
- Host: GitHub
- URL: https://github.com/KaiyuanZh/awesome-graph-representation-learning
- Owner: KaiyuanZh
- Created: 2019-03-04T05:41:42.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-10-13T02:17:47.000Z (about 4 years ago)
- Last Synced: 2024-05-23T07:53:05.024Z (7 months ago)
- Topics: adversarial-learning, awesome-list, graph, graph-convolutional-networks, graph-embedding, graph-mining, graph-neural-networks, graph-representation-learning
- Homepage:
- Size: 3.91 KB
- Stars: 66
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
Awesome Lists containing this project
- Awesome-Paper-List - Graph Representation Learning
- ultimate-awesome - awesome-graph-representation-learning - A curated list of awesome graph representation learning. (Other Lists / PowerShell Lists)
README
# Awesome Graph Representation Learning [](https://github.com/sindresorhus/awesome) [](http://makeapullrequest.com)
A curated list of awesome graph representation learning, inspired by [Awesome Adversarial Machine Learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning)
## Table of Contents
- [Papers](#papers)
- [Graph representation learning](#Graph-representation-learning)
- [Adversarial learning on graphs](#Adversarial-learning-on-graphs)
- [Tutorials and Workshops](#Tutorials-and-Workshops)
- [Adversarial learing](#Adversarial-learing-1)
- [Graph representation learning](#Graph-representation-learning-1)
## Papers
### Graph representation learning
- [Deep Convolutional Networks on Graph-Structured Data](https://arxiv.org/pdf/1506.05163), Mikael Henaff et al., arXiv 2015
- [Representation Learning on Graphs: Methods and Applications](https://arxiv.org/pdf/1709.05584), William L. Hamilton et al., arXiv 2018
- [Deep Learning on Graphs: A Survey](https://arxiv.org/pdf/1812.04202), Ziwei Zhang et al., arXiv 2018
- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/pdf/1812.08434), Jie Zhou et al., arXiv 2019
- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/pdf/1901.00596), Zonghan Wu et al., arXiv 2019
- [A Structural Graph Representation Learning Framework](http://ryanrossi.com/pubs/WSDM20-structural-node-embedding-framework.pdf), Ryan A. Rossi et al., WSDM 2020
- [Initialization for Network Embedding: A Graph Partition Approach](https://arxiv.org/abs/1908.10697), Wenqing Lin et al., WSDM 2020
- [Dynamic graph representation learning via self-attention networks](https://arxiv.org/abs/1812.09430), Aravind Sankar et al., WSDM 2020
- [Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders](https://arxiv.org/abs/1911.05465), Carl Yang et al., WSDM 2020
- [Robust Graph Neural Network Against Poisoning Attacks via Transfer Learning](https://arxiv.org/abs/1908.07558), Xianfeng Tang et al., WSDM 2020
### Adversarial learning on graphs
- [Intriguing properties of neural networks](https://arxiv.org/pdf/1312.6199), Christian Szegedy et al., arXiv 2014
- [Explaining and Harnessing Adversarial Examples](https://arxiv.org/pdf/1412.6572), Ian J. Goodfellow et al., ICLR 2015
- [Motivating the Rules of the Game for Adversarial Example Research](https://arxiv.org/pdf/1807.06732), Justin Gilmer et al., arXiv 2018
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://arxiv.org/pdf/1712.03141), Battista Biggio et al., arXiv 2018
- [Adversarial Attack on Graph Structured Data](https://arxiv.org/pdf/1806.02371), Hanjun Dai et al., ICML 2018
- [Adversarial Attacks on Neural Networks for Graph Data](https://arxiv.org/pdf/1805.07984.pdf), Daniel Zügner et al., KDD 2018
- [Adversarial Attacks on Node Embeddings](https://arxiv.org/pdf/1809.01093), Aleksandar Bojchevski et al., arXiv 2018
- [Data Poisoning Attack against Unsupervised Node Embedding Methods](https://arxiv.org/pdf/1810.12881.pdf), Mingjie Sun et al., arXiv 2018
- [Attack Graph Convolutional Networks by Adding Fake Nodes](https://arxiv.org/pdf/1810.10751), Xiaoyun Wang et al., arXiv 2018
- [Link prediction adversarial attack](https://arxiv.org/pdf/1810.01110), Jinyin Chen et al., arXiv 2018
- [Fast gradient attack on network embedding](https://arxiv.org/pdf/1809.02797), Jinyin Chen et al., arXiv 2018
- [Characterizing Malicious Edges targeting on Graph Neural Networks](https://openreview.net/pdf?id=HJxdAoCcYX), Xiaojun Xu et al., openreview 2018
- [Adversarial Attacks on Graph Neural Networks via Meta Learning](https://openreview.net/pdf?id=Bylnx209YX), Daniel Zügner et al., ICLR 2019
## Tutorials and Workshops
### Graph representation learning
- [KDD 2018 Graph Representation Tutorial]( https://ivanbrugere.github.io/kdd2018/)
- [WWW 2018 Representation Learning on Networks Tutorial](http://snap.stanford.edu/proj/embeddings-www/)
- [AAAI 2019 Graph Representation Learning Tutorial ](https://jian-tang.com/files/AAAI19/aaai-grltutorial-part0-intro.pdf)
- [Graph Representation Learning Book](https://www.cs.mcgill.ca/~wlh/grl_book/files/GRL_Book.pdf)
### Adversarial learing
- [NeurIPS 2016 Workshop on Adversarial Training](https://sites.google.com/site/nips2016adversarial/)
- [AAAI 2018 Adversarial machine learning tutorial](https://aaai18adversarial.github.io/)
- [AAAI 2019 Adversarial machine learning tutorial](https://aaai19adversarial.github.io/index.html#)
## Licenses
License
[](http://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, [Kaiyuan Zhang](https://kaiyuanzhang.com) has waived all copyright and related or neighboring rights to this work.