(Deep) Machine Learning on Graphs

This page provides some useful resources about (deep) machine learning on Graphs.

Workshops:

• The First International Workshop on Deep Learning on Graphs: Methodologies and Applications (DLGMA’20) [In Conjunction with AAAI 2020] — website

• ICCV 2019 Workshop: Scene Graph Representation and Learning (website, PDF if the workshop site is not retrievable)

• ICML 2019 Worshop on Learning and Reasoning with Graph-Structured Data (PDF)
• The First International Workshop on Deep Learning on Graphs: Methods and Applications (DLG’19) — In Conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (PDF) — Latex Template (ACM Conference Proceedings – New Template)
• MLG 2019 – 15th International Workshop on Mining and Learning with Graphs (PDF)

 

• SDM’19 Workshop on Deep Learning for Graphs (PDF)
• International Workshop on Deep Learning for Graphs and Structured Data Embedding
• 1st International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs 2019)
• 15th International Workshop on Mining and Learning with Graphs (co-located with KDD 2019)

Papers:

• Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584. (PDF link)
• Zhang, Z., Cui, P., & Zhu, W. (2018). Deep learning on graphs: A survey. arXiv preprint arXiv:1812.04202. (PDF link)

Good websites:

• Geometric Deep Learning.com
• Octavian.ai (github, Talks with slides, Posts)

Good Posts:

• • Graph-based machine learning: Part I
  • Graph-based machine learning: Part 2
  • How to get started with machine learning on graphs (Dec 6, 2018 by David Mack )
  • Graph Embeddings — The Summary (PDF)

Books:

• Graph-Powered Machine Learning (TOC in detail)

• TBA

Talks:

• Introduction to graphs and machine learning (PDF)