Graph Machine Learning

Graph Neural Networks: Algorithms and Applications A great presentation by Jian Tang about GNN basics, training many layers, self-supervised learning and statistical relational learning.

Foundations of Graph Neural Networks Course A new upcoming course by Zak Jost (you may remember his videos on GNNs) on the foundations of GNN which covers such topics as - Neural Message Passing - Fourier Transforms, Graph Wavelets and Spectral Convolutions - Permutation Symmetries - Representational capacity of GNNs - Graph fundamentals like the Laplacian and graph isomorphism.

Fresh picks from ArXiv This week on ArXiv: time series recovery, GNN challenge winning solutions, and benchmark for scene graph generation 🌳 If I forgot to mention your paper, please shoot me a message and I will update the post. Applications * Multivariate Time Series Imputation by Graph Neural Networks * Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction * Graph Constrained Data Representation Learning for Human Motion Segmentation * The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks * Image Scene Graph Generation (SGG) Benchmark * Structack: Structure-based Adversarial Attacks on Graph Neural Networks

GNN User Group Videos Videos from the last Thursday meeting of GNN user group are available now. This includes updates of DGL library, storing node feature for large graphs, and locally private GNNs.

Graph Machine Learning research groups:Shuiwang Ji I do a series of posts on the groups in graph research, previous post is here. The 32nd is Shuiwang Ji, a professor at Texas A&M University. His teams were awarded at OGB-LSC and AI Cures challenges. He also recently advised graph libraries such as MoleculeX and DIG. Shuiwang Ji (~1982) - Affiliation: Texas A&M University - Education: Ph.D. at Arizona State University in 2008 (advisor: Jieping Ye) - h-index 44 - Interests: GNNs, self-supervised learning, surveys, libraries. - Awards: best papers at KDD, WWW, ACM Distinguished Member

Graph Convolutional Neural Networks to Analyze Complex Carbohydrates A blog post by Daniel Bojar about an application of GNN to analyzing glycan sequences and their proposed GNN architecture called SweetNet. There are other coverages of this work (here and here). The paper is here and the code is here.

Header-Only C++ Library for Graph Representation and Algorithms In case you need the speed of C++ for the well-known graph algorithms there is a nice repo that collects many of them.

Graph Neural Networks User Group: July meeting This month GNN user group talks about a new release of DGL and applications of GNNs. Please join this Thursday! 4:00 - 4:15 PM (PDT): DGL 0.7 release(Dr. Minjie Wang, Amazon) 4:15 - 4:30 PM (PDT): Storing Node Features in GPU memory to speedup billion-scale GNN training (Dr. Dominique LaSalle, NVIDIA) 4:30 - 5:00 PM (PDT): Locally Private Graph Neural Networks (Sina Sajadmanesh, Idiap Research Institute, Switzerland). 5:00 - 5:30 PM (PDT): Graph Embedding and Application in Meituan (Mengdi Zhang, Meituan).

Fresh picks from ArXiv This week on ArXiv: SOTA for protein energy prediction, another solution to OGB-LSC challenge, and a new dataset based on Wikipedia 📚 If I forgot to mention your paper, please shoot me a message and I will update the post. Applications * X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering * Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity KDD 2021 GNNs * Local2Global: Scaling global representation learning on graphs via local training * Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks with William L. Hamilton * Bridging the Gap between Spatial and Spectral Domains: A Theoretical Framework for Graph Neural Networks * Large-scale graph representation learning with very deep GNNs and self-supervision with Petar Veličković * Group Contrastive Self-Supervised Learning on Graphs with Shuiwang Ji Datasets * WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset with Oriol Vinyals

Interpretable Deep Learning for New Physics Discovery In this video, Miles Cranmer (Princeton) discusses a method for converting a neural network into an analytic equation using a particular set of inductive biases. The technique relies on a sparsification of latent spaces in a deep neural network, followed by symbolic regression. In their paper, they demonstrate that they can recover physical laws for various simple and complex systems. For example, they discover gravity along with planetary masses from data; they learn a technique for doing cosmology with cosmic voids and dark matter halos; and they show how to extract the Euler equation from a graph neural network trained on turbulence data.

labml.aiAnnotated PyTorch Paper Implementations A very cool collection of popular deep learning blocks, nicely formatted in the browser with extensive comments. Among others there is a GAT implementation.

Awesome Explainable Graph Reasoning An awesome collection of research papers and software related to explainability in graph machine learning, provided by AstraZeneca. It covers papers on explainable predictions and reasoning, libraries, and survey papers.