Graph Machine Learning

Graph Papers at ICML 2021 ICML 2021 is happening this week and here is a list of all relevant graph papers that you can encounter there. There are papers on improving expressiveness, explainability, robustness, normalization and theory.

Fresh picks from ArXiv This week on ArXiv: QA in images, graph matching, and learning robot dynamics 🤖 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN * Reasoning-Modulated Representations with Petar Veličković and Thomas Kipf * SENSORIMOTOR GRAPH: Action-Conditioned Graph Neural Network for Learning Robotic Soft Hand Dynamics * Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More with Stephan Günnemann * Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering with Stephan Günnemann * Elastic Graph Neural Networks Survey * A Survey of Knowledge Graph Embedding and Their Applications

Effortless Distributed Training of Ultra-Wide GCNs A great post about distributed training of GNNs on large graphs. The architecture splits the GNN into several submodules where each is trained independently on separate GPUs, providing the flexibility to increase significantly the hidden dimension of embeddings. As such this approach is GCN model agnostic, compatible with existing sampling methods, and performs the best in very large graphs.

LOGML Videos LOGML is an exciting summer school with projects and talks about graph ML happening this week. A collection of videos that includes presentations of the cutting edge research as well as industrial applications from leading companies are available now for everyone.

Speeding Up the Webcola Graph Viz Library with Rust + WebAssembly A captivating story about optimizing visualization of graphs in the browser. The code can be found here. Here is a performance comparison of different browser visualization libraries. And here is another efficient library for plotting graphs in a browser.

GNN User Group Meeting videos(June) Video from the June meeting of GNN user group that includes talks about binary GNNs and dynamic graph models by Mahdi Saleh and and about simplifying large-scale visual analysis of tricky data & models with GPUs, graphs, and ML by Leo Meyerovich.

Fresh picks from ArXiv This week on ArXiv: explanations of GNNs, generalization of GAE and generating natural proofs 👴 If I forgot to mention your paper, please shoot me a message and I will update the post. GNN *Automated Graph Learning via Population Based Self-Tuning GCN * Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property Prediction * Robust Counterfactual Explanations on Graph Neural Networks * Probabilistic Graph Reasoning for Natural Proof Generation * On Generalization of Graph Autoencoders with Adversarial Training * Private Graph Data Release: A Survey

Graph Machine Learning research groups:Andreas Krause I do a series of posts on the groups in graph research, previous post is here. The 31st is Andreas Krause, a professor at ETH Zurich and an advisor for Stefanie Jegelka. Andreas Krause (~1982) - Affiliation: ETH Zurich - Education: Ph.D. at CMU in 2008 (advisor: Carlos Guestrin) - h-index 81 - Interests: social network analysis, community detection, graphical models. - Awards: Rossler Prize, best papers (AISTATS, AAAI, KDD, ICML)

How to build E(n) Equivariant Normalizing Flows, for points with features? A nice post that discusses how one can use normalizing flows and equivariant GNNs to generate realistic molecules.

Fresh picks from ArXiv This week on ArXiv: WL to solve planar graphs, efficient molecule generation, and compressing graphs 🤐 If I forgot to mention your paper, please shoot me a message and I will update the post. Math Logarithmic Weisfeiler-Leman Identifies All Planar Graphs GNNs Curvature Graph Neural Network Relational VAE: A Continuous Latent Variable Model for Graph Structured Data GraphPiece: Efficiently Generating High-Quality Molecular Graph with Substructures Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective KDD 2021 Partition and Code: learning how to compress graphs with Andreas Loukas and Michael M. Bronstein Evolving-Graph Gaussian Processes ICML Workshop 2021

Connecting the Dots: Harness the Power of Graphs & ML A new short book on graph ML that describes various algorithms on graphs and future challenges.

GNN Applications An overview presentation by Xavier Bresson about applications of GNNs, which include chip design, protein folding, autonomous driving, energy physics, and more.