Graph Machine Learning

GNN Tutorial & Graph Convolution Intuition @ Distill Distill.pub is a great new resource aimed at re-defining a way we publish papers. Publications on Distill have rich visualizations and hands-on examples that you can tweak right in a browser. Unfortunately, Distill goes on a hiatus. But, as the last bow, the authors prepared two very cool articles breaking down message passing and graph convolutions: 1. A Gentle Introduction to Graph Neural Networks 2. Understanding Convolutions on Graphs Something you definitely do not want to miss in September!

Fresh picks from ArXiv This week on ArXiv: predictions of routing times, benchmarking architecture tricks, and drug repurposing study 💊 If I forgot to mention your paper, please shoot me a message and I will update the post. Conferences * Single Node Injection Attack against Graph Neural Networks CIKM 2021 * ETA Prediction with Graph Neural Networks in Google Maps CIKM 2021, with Petar Veličković * Tree Decomposed Graph Neural Network CIKM 2021, with Tyler Derr * DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN CIKM 2021 * Multiplex Graph Neural Network for Extractive Text Summarization EMNLP 2021 * Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding IEEE BioCAS 2021 * DC-GNet: Deep Mesh Relation Capturing Graph Convolution Network for 3D Human Shape Reconstruction ACM MM'21 * Visualizing JIT Compiler Graphs GD 2021 GNNs * Spatio-Temporal Graph Contrastive Learning Benchmark * Weisfeiler-Leman in the BAMBOO: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity TACL 2021 * Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Math * Smallest graphs with given automorphism group

Graph Drawing and Network Visualization 2021 A symposium on graph Drawing and network visualization is a nice niche conference on how to draw graphs efficiently and insightfully. This year it will be organized both online and offline (in Tübingen, Germany). Dates are: September 14-17, 2021. Accepted papers can be seen here.

Video: Graph Neural Networks - a perspective from the ground up A beautiful video about GNNs aimed at CS undergrads that explains what message passing and node embeddings are and gives a link prediction example.

Fresh picks from ArXiv This week on ArXiv: GNNs for lidars, complex reasoning in knowledge graphs, and building AI for drawing graphs 🤖 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection * TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction * Learning to Match Features with Seeded Graph Matching Network * EqGNN: Equalized Node Opportunity in Graphs Applications * GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network * Adaptive Graph Convolution for Point Cloud Analysis * Hyperbolic Hypergraphs for Sequential Recommendation * Implementation of Sprouts: a graph drawing game Knowledge graphs * Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs * UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text

TorchDrug: a powerful and flexible machine learning platform for drug discovery Jian Tang and his co-workers from MILA open-sourced a new library TorchDrug on drug modeling with machine learning. It includes an easy interface for property prediction, pretrained molecular representations, de-novo molecule design & optimization, knowledge graph reasoning, and more.

Graph Machine Learning research groups: Ian Davidson I do a series of posts on the groups in graph research, previous post is here. The 33rd is Ian Davidson, a professor at UC Davis, who works in the areas with societal impacts such as neuroscience, intelligent tutoring systems and social networks. Ian Davidson (~1973) - Affiliation: UC Davis - Education: Ph.D. at Monash University in 2000 (advisor: C.S. Wallace) - h-index 44 - Interests: fairness, clustering, graphical models. - Awards: best papers at KDD, SIAM, ICDM

Book: Designing and Building Enterprise Knowledge Graphs (Synthesis Lectures on Data, Semantics, and Knowledge) A new book by Ora Lassila and Juan Sequeda that guides on designing and building knowledge graphs from enterprise relational databases in practice. It presents a principled framework centered on mapping patterns to connect relational databases with knowledge graphs, the roles within an organization responsible for the knowledge graph, and the process that combines data and people. The content of this book is applicable to knowledge graphs being built either with property graph or RDF graph technologies.

Awesome Efficient Graph Neural Networks A new awesome repo by Chaitanya K. Joshi with the curated list of must-read papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications.

Fresh picks from ArXiv This week on ArXiv: PDE-inspired GNNs, proves to the conjectures, and a new benchmark for graph completion 🧵 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Distilling Holistic Knowledge with Graph Neural Networks ICCV 2021 * Fully Hyperbolic Graph Convolution Network for Recommendation CIKM 2021 * Jointly Attacking Graph Neural Network and its Explanations * LEO: Learning Energy-based Models in Graph Optimization * PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations * Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data with Bryan Perozzi * AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting Math * Isomorphisms between random graphs * Edge Partitions of Complete Geometric Graphs (Part 1) Survey * Are Missing Links Predictable? An Inferential Benchmark for Knowledge Graph Completion * Influence Maximization in Social Networks: A Survey of Behaviour-Aware Methods

GDL Course A course that follows closely the geometric deep learning book. It contains 12 lectures, 2 tutorials, and 4 seminars covering topics such as graphs, sets, grids, groups, geodesics, gauges, and time warping. Videos and slides are available.

Essays on Data Science A great collection of blog posts on machine learning and computer science covering topics such as infinitely wide neural nets, markov models, and graph deep learning.

Knowledge Graphs in Natural Language Processing @ ACL 2021 A regular update from Michael Galkin on the SOTA applications of KG in the world of words: Neural Databases & Retrieval KG-augmented Language Models KG Embeddings & Link Prediction Entity Alignment KG Construction, Entity Linking, Relation Extraction KGQA: Temporal, Conversational, and AMR.

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.