Graph Machine Learning

DeepSnap There is a release of DeepSnap by Stanford group. I have not tested it, but it should allow applying graph algorithms from networkx to pytorch-geometric graphs.

Graph Machine Learning research groups: Tommi Jaakkola I do a series of posts on the groups in graph research, previous post is here. The eighth is Tommi Jaakkola. He has 7 papers in upcoming ICML 2020. His recent interests include molecular graph design and he maintains AI initiative for finding promising antiviral molecules for COVID-19. Tommi Jaakkola (~1971) - Affiliation: MIT - Education: Ph.D. at MIT in 1997 (supervised by Michael Jordan); - h-index: 76; - Awards: Sloan research fellowship, AAAI Fellow; - Interests: molecular generation, models of GNN

PhD Theses on Graph Machine Learning Here are some PhD dissertations on GML. Part 2 (previous here). Haggai Marron: Deep and Convex Shape Analysis Benoit Playe: Machine learning approaches for drug virtual screening

June Arxiv: how many graphs papers? From 18 March to 17 April there were 282 new and 98 updated papers in ArXiv CS section. This is 18 papers less that in the previous period.

Optimal transport: a hidden gem that empowers today’s machine learning Very simple explanation of what optimal transport problem is and how it can be applied to various domains such as computer vision. Interestingly just yesterday there was a paper on optimal transport GNN.

Deep learning on graphs: successes, challenges, and next steps The first blog post of Michael Bronstein about graph learning.

ICML 2020. Comprehensive analysis of authors, organizations, and countries. Finally here is my post on the analysis of ICML 2020. There are several things I learned from that. For example that USA participates in 3/4 of the papers 😱 Or that DeepMind makes approximately half of all papers for UK. Or that Google does not collaborate with other companies. Or that, except the USA, there is only China that can brag about several companies that publish regularly. Or that a Japanese professor published 12 papers. And much more. The code and data is on the github, but the cool part is that you can make your own interactive plots in colab notebook (with no installation required) including a collaboration graph between universities and companies.

Fresh picks from ArXiv This week people share their works they submitted to NeurIPS, a lot of interesting papers from the top people in GML. GNN • From Graph Low-Rank Global Attention to 2-FWL Approximation with Yaron Lipman • Graph Meta Learning via Local Subgraphs with Marinka Zitnik • Data Augmentation for Graph Neural Networks • Towards Deeper Graph Neural Networks with Differentiable Group Normalization • Learning Graph Models for Template-Free Retrosynthesis with Regina Barzilay • Wide and Deep Graph Neural Networks with Distributed Online Learning • Pointer Graph Networks with Petar Veličković • Optimal Transport Graph Neural Networks with Octavian-Eugen Ganea, Regina Barzilay, Tommi Jaakkola • Manifold structure in graph embeddings • On the Bottleneck of Graph Neural Networks and its Practical Implications Conferences • Exploring Algorithmic Fairness in Robust Graph Covering Problems NeurIPS 2019 Surveys • A Survey on Generative Adversarial Networks: Variants, Applications, and Training

The ‘Useless’ Perspective That Transformed Mathematics Matrix algebra is well understood, while group theory, which is used in many proofs of graph theory and other fields, is much more complicated to study. Representation theory creates a bridge between group theory and linear algebra by assigning a matrix to each element in a group, according to certain rules. This nice article introduces to the world of representation theory.

Covid Knowledge Graph A knowledge graph on COVID-19 that integrates various public datasets. This includes relevant publications, case statistics, genes and functions, molecular data and much more. It's implemented in Neo4j and can be accessed via browser.