Graph Machine Learning

Fresh picks from ArXiv This week on ArXiv: generalization guarantees, explaining bio recommendations, and learning over cosmos data 🌕 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * Generalizing Graph Neural Networks on Out-Of-Distribution Graphs * Federated Social Recommendation with Graph Neural Network with Philip S. Yu * Pre-training Graph Neural Network for Cross Domain Recommendation with Philip S. Yu * Inferring halo masses with Graph Neural Networks * Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs Benchmark * GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation Hardware * QGTC: Accelerating Quantized GNN via GPU Tensor Core

Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology And Michael is back with a first post in the series of posts on the connection between GML and differential geometry and algebraic topology. We've been waiting for this!

Introducing TensorFlow Graph Neural Networks A new API for TF2 to build GNNs. It would be interesting to see how it compares to PyG and DGL libraries.

Complex and Simple Models of Multidimensional Data : from graphs to neural networks A mini-workshop on applications of graphs in biology. 1 December, free, but registration is mandatory.

Fresh picks from ArXiv This week on ArXiv: knowledge distillation, robustness benchmark, and SVD instead of learning ✒️ If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * On Representation Knowledge Distillation for Graph Neural Networks * Can Graph Neural Networks Learn to Solve MaxSAT Problem? * DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks NeurIPS 2021 * An Interpretable Graph Generative Model with Heterophily * Convolutional Neural Network Dynamics: A Graph Perspective with Danai Koutra Benchmarks * Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning GRL * Implicit SVD for Graph Representation Learning NeurIPS 2021 * Multi-task Learning of Order-Consistent Causal Graphs with Le Song * Adversarial Attacks on Graph Classification via Bayesian Optimisation * Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation CIKM 2021

From Mila with 💌 and graphs A prolific week for Mila researchers: - Michael Galkin released a new review of Knowledge Graph papers from EMNLP 2021. For those of us who didn't make it to Dominical Republic, you can experience the premium Punta Cana content about applications of graphs in language modeling, KG construction, entity linking, and question answering. - Best Long Paper award at EMNLP 2021 went to Visually Grounded Reasoning across Languages and Cultures by the team from Cambridge, Copenhagen, and Mila Mila and Mila-affiliated folks run a good bunch of reading groups you might find useful: in addition to the GRL Reading Group and LoGaG Reading group, there exist ones on Neural AI, Out-of-Distribution Generalization, Quantum & AI , ML4Code

COLLOQUIUM PRAIRIE A colloquium on AI, which among others talks about graph ML. Next talk will be on "Exploiting Graph Invariants in Deep Learning" by Marc Lelarge (Inria).

Graph papers and reviews at ICLR 2022 Here is a list of reviews on graph papers at ICLR 2022. Three papers received the average score of 8. For more stats refer here.

Graph Databases Blog Posts 4 blog posts exploring different ideas behind graph databases: * Graph Fundamentals — Part 1: RDF * Graph Fundamentals — Part 2: Labelled Property Graphs * Graph Fundamentals — Part 3: Graph Schema Languages * Graph Fundamentals — Part 4: Linked Data

Papers with Code newsletter: GNN The 19th issue of the Papers with Code newsletter covers a brief update of the latest developments in graph neural networks (GNNs), a list of recent applications of GNNs, top trending papers for October 2021 on Papers with Code.

Graph papers at NeurIPS 2021 There are ~140 graph papers which can be found here, which is about 6% of all 2334 papers. More statistics can be found here.

Fresh picks from ArXiv This week on ArXiv: uncertainty for node classification, GNNs for tabular data, and cryptocurrency graph 🤑 If I forgot to mention your paper, please shoot me a message and I will update the post. GNNs * A Scalable AutoML Approach Based on Graph Neural Networks * Optimizing Sparse Matrix Multiplications for Graph Neural Networks * Graph Embedding with Hierarchical Attentive Membership WSDM 2022 * Unbiased Graph Embedding with Biased Graph Observations * Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification with Stephan Günnemann * Robustness of Graph Neural Networks at Scale with Stephan Günnemann * VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization * Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods * Nested Graph Neural Networks with Pan Li * Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features * Tackling Oversmoothing of GNNs with Contrastive Learning * On the Power of Edge Independent Graph Models * On Provable Benefits of Depth in Training Graph Convolutional Networks * UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation CIKM'2021 * Finding a Concise, Precise, and Exhaustive Set of Near Bi-Cliques in Dynamic Graphs WSDM 2022 * How to transfer algorithmic reasoning knowledge to learn new algorithms? with Petar Velickovic and Jian Tang Software * retworkx: A High-Performance Graph Library for Python * NeuroComb: Improving SAT Solving with Graph Neural Networks * Graph? Yes! Which one? Help! Datasets * Towards a Taxonomy of Graph Learning Datasets * Cryptocurrencies Activity as a Complex Network: Analysis of Transactions Graphs