Graph Machine Learning

Graph Representation Learning Reading Group @ Mila The more reading groups on Graph ML in different regions and timezones - the better! This one is organized by Mila postdocs and open for participation via Zoom. RG starts this Thursday. The lineup for next weeks is published, check the website for more details.

Fresh picks from ArXiv This week on ArXiv: reconstruction conjecture for higher expressivity, decision graphs, and control in robots 🤖 If I forgot to mention your paper, please shoot me a message and I will update the post. NeurIPS * Motif-based Graph Self-Supervised Learning for Molecular Property Prediction NeurIPS 2021 * Reconstruction for Powerful Graph Representations NeurIPS 2021 * Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration NeurIPS 2021 GNNs * Graph Pointer Neural Networks * Equivariant Neural Network for Factor Graphs * Tree in Tree: from Decision Trees to Decision Graphs Applications * Deep Fraud Detection on Non-attributed Graph * How Neural Processes Improve Graph Link Prediction * Coverage Control in Multi-Robot Systems via Graph Neural Networks * Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs

Scalable Algorithms for Semi-supervised and Unsupervised Learning A great event coming from Google, Oct 5-7 on unsupervised learning, which includes many great speakers from graph community (Andreas Krause, Piotr Indyk, David Woodruff, David Gleich, Stefanie Jegelka, Leman Akoglu, Danai Koutra, Andreas Loukas, Marinka Zitnik, and many others).

Graph Neural Networks for Point Cloud Processing: meeting An online talk on 4th October by Mahdi Saleh on their recent work Graphite: GRAPH-Induced feaTure Extraction for Point Cloud Registration. In this presentation he discusses how graphs can be utilized to describe point cloud patches, detect salient points and use them in downstream tasks such as 3D registration.

Feed-forward neural networks for graph processing: video In this video, Charu Aggarwal discusses the simplest approach of using feedforward neural networks for graph processing. Much simpler than convolutional neural networks, they can do surprisingly well for creating node representations. The presentation is closely related to node2vec, but simplifies the presentation in many respects.

Fresh picks from ArXiv This week on ArXiv: generalization of graph embeddings, approximate message passing, and GNNs for hadron collider 🚇 If I forgot to mention your paper, please shoot me a message and I will update the post. Knowledge graphs * How Does Knowledge Graph Embedding Extrapolate to Unseen Data: a Semantic Evidence View GNNs * Graph-based Approximate Message Passing Iterations * Orthogonal Graph Neural Networks * Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits Applications * Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction * GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction

Kite: An interactive visualization tool for graph theory Another tool called Kite to draw simple graphs and run some graph algorithms.

Graph Machine Learning in Industry workshop live Our workshop starts in one hour and I'm excited about our speakers and talks that are ahead (something I would like to attend even if I didn't organize it). You can join us on YouTube or Zoom and we encourage you to ask questions. The topics are: 0. Me (17:00 Paris time): opening remarks 1. James Zhang (AWS) (17:15): Challenges and Thinking in Go-production of GNN + DGL. 2. Charles Tapley Hoyt (Harvard) (17:45): Current Issues in Theory, Reproducibility, and Utility of Graph Machine Learning in the Life Sciences. 3. Anton Tsitsulin (Google) (18:15): Graph Learning for Billion Scale Graphs. 4. Cheng Ye (AstraZeneca) (19:00): Predicting Potential Drug Targets Using Tensor Factorisation and Knowledge Graph Embeddings. 5: Rocío Mercado (MIT) (19:30): Accelerating Molecular Design Using Graph-Based Deep Generative Models. 6. Lingfei Wu (JD.com) (20:00): Deep Learning On Graphs for Natural Language Processing.

GML Express: Graph ML in Industry Workshop, Geometric Deep Learning, and New Software. In case you missed recent most popular events in graph ML, here is a fresh newsletter with recent videos, courses, books, trends, and future events.

OGB Large-Scale Challenge Workshop - Presentations of the Winners OGB LSC is a KDD'21 challenge organized by the OGB team and known for the largest-to-date benchmarking datasets in node-level (240M nodes / 1.7B edges), link-level (90M nodes, 500M edges), and graph-level (4M molecules) tasks. Surely, not all academic labs can afford such compute, but the more interesting are the approaches taken by the winners! Are there any smart tricks or merely "more layers - more ensembles - GPUs go brrr"? Finally, the recordings of the LSC workshop are available! (~3 hours long, so the Graph ML channel editors assume you've already successfully digested the ML Street Talk for breakfast) The 2nd day of the workshop features (videos are available): - Invited talks by Viktor Prasanna (USC), Marinka Zitnik (Harvard), and Larry Zitnick (Facebook AI) - Panel discussion on the future of Graph ML with Yizhou Sun (UCLA), Zheng Zhang (NYU / Amazon), Shuiwang Ji (Texas A&M), and Jian Tang (MILA)

Fresh picks from ArXiv This week on ArXiv: demystifying performance of hyperbolic embeddings, complex question answering, and emotion chatbots 👧 If I forgot to mention your paper, please shoot me a message and I will update the post. Knowledge graphs * Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs * Complex Temporal Question Answering on Knowledge Graphs * Emily: Developing An Emotion-affective Open-Domain Chatbot with Knowledge Graph-based Persona Benchmarking * Comparing Euclidean and Hyperbolic Embeddings on the WordNet Nouns Hypernymy Graph GNNs * Releasing Graph Neural Networks with Differential Privacy Guarantees

Geometric Deep Learning @ML Street Talk Michael Bronstein, Petar Veličković, Taco Cohen and Joan Bruna are special guests in the new 3.5 hours (👀) episode of ML Street Talk talking Geometric DL and explaining the concepts covered in their recent book and pretty much all the current state of the art in the field. Available on YT as a video and as a podcast on all major platforms.