Graph Machine Learning

​​Call for papers for the ICCV Workshop on Scene graphs and graph representation learning Guest post by Azade Farshad We invite you to submit your graph related work to the ICCV Workshop on Scene graphs and graph representation learning. The workshop will feature talks by Fei Fei Li (Stanford), Luca Carlone (MIT), Bernard Ghanem (KAUST), Nicolas Padoy (Uni Strasbourg), Emanuele Rodola (Sapienza Rome), Hanwang Zhang (NTU), and Helisa Dhamo (Huawei). Deadlines and Important Dates: - Paper submission deadline: July 25th, 2023 (11:59 PM Anywhere on Earth) - Notification to Authors: August 8th, 2023 - Camera-ready Deadline: August 19th, 2023 Submission instructions are available on the SG2RL website: https://sg2rl.github.io/ Workshop date: October 2, 2023

Graph ML News (July 15th) - NeurIPS workshops, M2Hub 🪛 NeurIPS’23 announced the list of accepted workshops! Graph learning is well-presented, you might want to have a look at: - AI for Accelerated Materials Design (AI4Mat-2023) - AI for Science: from Theory to Practice - Machine Learning and the Physical Sciences - Machine Learning in Structural Biology Workshop - New Frontiers in Graph Learning (GLFrontiers) - New Frontiers of AI for Drug Discovery and Development - Symmetry and Geometry in Neural Representations - Temporal Graph Learning Workshop We will keep an eye on the submission deadlines, generally you might expect them to be somewhere around the NeurIPS accepted papers announcement. 💠M2Hub is a fresh collection of datasets and models for materials discovery: 11 datasets spanning organic and inorganic molecules and crystals, 8 models including EGNN, Equiformer, DimeNet, and GemNet, and more experiments in the fresh preprint. Materials discovery is catching up with drug discovery! 📈UniMol, a 3D framework for molecular representation learning, has updated the performance of UniMol+ showing strong performance on OGB PCQM4M v2 and OpenCatalyst (OC20 IS2RE). Looks like this year’s OGB Large Scale challenge and Open Catalyst challenge are going to have a heated competition, eg, having in mind a recently released EquiformerV2. 🔬Simone Scardapane (Sapienza) prepared a nice slide deck on Designing and Explaining GNNs - the second half of the deck is about current explainability methods, have a look if you work in this area. Weekend reading: M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery An OOD Multi-Task Perspective for Link Prediction with New Relation Types and Nodes

GraphML News (July 7th) - Generative Chemistry, Temporal Graph Benchmark Lots of news this week! 🔬 Starting with new blog posts, Charlie Harris wrote an article on Diffusion Models in Generative Chemistry for Drug Design covering the basics of denoising diffusion and score-based generative modeling going into molecular usecases with Equivariant Diffusion, DiffSBDD, DiffDock, and raising questions about fair evaluation of generative models vs standard tools. Looking more from the industrial perspective, Leo Wossnig published a piece Where is generative design in drug discovery today discussing successes and failures of generative approaches and highlighting the main obstacles for ML folks wishing to dive into drug discovery: (1) data scarcity (eg, no data for new targets), (2) slow experimental pipelines to generate new data, (3) end-to-end pipelines and tech stack in general. 🔧Graphium is the new library for molecular representation learning in the Datamol ecosystem. Graphium is packed with latest algorithms (like Random Walk Structural Encodings) and ML models (like recent GPS++, the winner of OGB LSC’22), and scales to large compute, you can even spin up training on Graphcore IPUs. 📏Temporal Graph Benchmark (TGB) is finally here! It was long awaited in the graph learning community that OGB needs a temporal branch, and TGB delivers dynamic link prediction and node property prediction datasets, standard loaders, evaluators, and, of course, the leaderboards (good old Temporal Graph Network is still a very strong baseline). Similarly to OGB, there are small, medium, and large graphs (the largest include about 1M nodes, 50M edges, 30M timesteps). More details can be found in the preprint by Shenyang Huang, Farimah Poursafaei, and the OGB gang. AStarNet, the scalable GNN for KG reasoning, can now be integrated right with ChatGPT to enhance its factual correctness. Given a textual query, AStarNet also runs graph inference on the backbone graph (Wikidata subset) and produces top reasoning paths supporting the answer. New foundation models: 1️⃣NSQL, a Copilot-like LM for SQL queries by Numbers Station, is openly available in 350M / 2B / 6B versions outperforming all existing open-source SQL models; 2️⃣xTrimo, 100B closed-source protein LM

GraphML News (July 1st) ⚛️ 3rd Open Catalyst challenge has been announced! This year the task is to predict the global minimum binding energy (adsorption energy) given an adsorbate and catalyst surface. The main dataset includes the new OC20-Dense split with roughly 15K initial structures and 3M frames. Baselines are GemNet-OC and Equivariant Spheric Channel Network (eSCN). Results will be announced at NeurIPS’23. 🎙️ The biennial Sampling Theory and Applications Conference (SAMPTA) 2023 will soon take place at Yale in July 10-14. This year will feature invited talks by Soledad Villar, Gitta Kutyniok, Michael Bronstein, Dan Spielman, and other prominent researchers. Registrations are still open! 🧬 Folks in comp bio might want to refresh the background on state space models (SSMs) - HyenaDNA, a collab between Stanford, Harvard, and Mila, is a DNA model with a whopping context length of up to 1M tokens of single nucleotides. HyenaDNA scales to lengths unattainable even by linear Transformers and shows SOTA of 23 genomic tasks. Pre-trained checkpoints are already on HuggingFace. The authors hint upon further generative applications, so we’ll keep an eye on that. A few words about hypergraphs: the anonymous author put on a tutorial on the basics of hypergraphs and building hypergraph GNNs. And a new work Hypergraph factorisation for multi-tissue gene expression imputation shows how to use message passing hypergraph NNs for processing gene expression in comp bio applications.

GraphML News (June 24th) A plenty of news, finally. There is going to be a GraphML community meetup at ICML’23 similar to that at ICML’22 and NeurIPS’22, feel free to drop by if you are at the conference. More details are to follow in the LoG2 slack. Michael Bronstein, Francesco Di Giovanni and Ben Gutteridge wrote a new blogpost on Dynamically Rewired Delayed Message Passing - an approach to address over-squashing and improve long-range capabilities of GNNs. vDREW is based on the idea of sparse skip connections where a node can have a direct access to its k-hop neighbors but delayed in time. Good to see our long-range LGRB dataset gaining more traction in the community 🙂 New PhD thesis: Cristian Bodnar, the author of message passing architectures using simplicial complexes, cellular complexes, and neural sheaves, published his work on Topological Deep Learning: Graphs, Complexes, Sheaves. This is an excellent introduction to topology and topological DL, highly recommended. LLMs can instruct robots to create chemical compounds: Andrew White presented a nice demo of ChemCrow - an agent that goes from natural language instructions to a sequence of real robotic actions to synthesize something. The authors synthesized 3 organocatalysts and even an insect repellent. Weekend reading: Equiformer V2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations feat. Tess Smidt - the next and faster version of the famous and quite popular Equiformer (ICLR’23). Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials feat. Shengchao Liu, Anima Anandkumar, and Jian Tang - introduces Geom3D, a massive suite of datasets and 2D/3D geometric models that work on molecules, proteins, and even crystals. The repo offers 10 datasets, 35 models, and 14 pre-training methods. QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules feat. Shuiwang Ji - QM9 with Hamiltonian matrices for 2.4K MD trajectories and 130K molecular geometries. Hyperbolic Representation Learning: Revisiting and Advancing feat. Rex Ying - a nice overview of hyperbolic GNNs in general and a new model, Hyperbolic Informed Embedding, in particular.

On the Connection Between MPNN and Graph Transformer Chen Cai, Truong Son Hy, Rose Yu, Yusu Wang from UCSD Invited post by Chen Cai Our work aims to understand the relationship between local GNN (MPNN) and global GNN (Graph Transformer). Here local GNN refers to MPNN that mix node features locally and global GNN refers to the graph transformer, which incorporates graph topology into positional embeddings (along with existing node/edge features) and send a set of feature vectors into a vanallia transformer. 1️⃣ Local MPNN and global Graph Transformer are two major paradigms of graph learning. MPNN comes early and encompasses several popular architectures such as GCN, GraphSage, and GAT. However, they are known to suffer from issues like limited expressive power, over-smoothing, and over-squashing. Graph Transformer, on the other hand, received a lot of attention recently and shows competitive performance on standard benchmarks like OGB and tasks that require long-ranging reasoning. Our goal is to gain a fine-grained understanding of the relationship between such two paradigms. 2️⃣ Previously, 1 showed that with specific positional embedding, GT can approximate 2-IGN Invariant Graph Networks, which is at least as expressive as MPNN. This work establishes an important link from global GNN to local GNN. 3️⃣ In our work, we tried to establish the link from the inverse direction: can MPNN approximate GT as well? We looked into a very simple way to incorporate global modeling into the local mixing of MPNN: virtual node (VN). MPNN + VN adds a virtual node and connects VN to all the graph nodes and then does (heterogenous) message passing on the modified graph. It is a heuristic proposed in the early days of GNN research and shows consistent improvement over MPNN. However, there is little theoretical understanding of MPNN + VN. 4️⃣ We have a set of approximation results of MPNN + VN on GT. 1) for constant depth and width MPNN + VN (O(1) depth and width), it can approximate self-attention layers of two “linear transformers”, Performer & Linear Transformer. 2) for wide MPNN + VN (O(1) depth O(n^d) width), we show it can approximate full GT via a link to equivariant Deepsets. 3) For deep MPNN + VN (O(n) depth O(1) width), it can approximate one self-attention layer. 5️⃣ On the experimental side, we pushed the limit of simple MPNN + VN and showed that 1) it works surprisingly well on LRGB (long-range graph benchmark) dataset where previously GT dominates. 2) leveraging the GraphGPS framework, our MPNN + VN improves over the previous implementation 3) MPNN + VN outperforms Linear Transformer and MPNN on the climate modeling task. See our paper and code for more details! Pure Transformers are Powerful Graph Learners

GraphML News (June 17th) -- Distributional Graphormer It seems researchers took a break after NeurIPS deadlines (or braced themselves with the 6-paper reviewing batches) and there hasn’t been that much news lately. Microsoft Research announced Distributional Graphormer, a massive generative model based on Graphormer suitable for many AI 4 Science tasks such as protein ligand binding, conformation transition pathway prediction, and even conditional crystal lattice generation (eg, generate a structure with a given band gap). Quoting the authors, “DiG attempts to predict the complicated equilibrium distribution of a given system by gradually transforming a simple distribution (e.g., a standard Gaussian) through the simulation of a predicted diffusion process that leads towards the equilibrium distribution.” The accompanying 80-pager preprint is a nice weekend reading 😉 Apart from that, the Learning on Graphs meetup took place in Paris with exciting keynotes, and Michael Bronstein received a prestigious UKRI Turing AI Fellowship (only two were given this year) to work on Graph ML algorithms inspired by physical systems. More new papers: Topological Singularity Detection At Multiple Scales (ICML’23) by Julius von Rohrscheidt and Bastian Rieck. Check out a nice Twitter thread by Bastian for a visual explanation. Generating Novel, Designable, and Diverse Protein Structures by Equivariantly Diffusing Oriented Residue Clouds (ICML’23) by Yeqing Lin, Mohammed AlQuraishi. Genie is 10x smaller than RFDiffusion but gets quite close in terms of generative performance! Have a look at the thread by Mohammed with fancy generated gifs. Rigid Body Flows for Sampling Molecular Crystal Structures feat. Pim de Haan and Frank Noé Enabling tabular deep learning when d≫n with an auxiliary knowledge graph feat. Hongyu Ren and Jure Leskovec

GraphML News (June 10th) Emanuele Rossi and Michael Bronstein published a new blog post on Directed GNNs. The idea is rather simple (different learnable aggregations for in- and out- neighbors) and shows very good results on heterophilic graphs. In fact, DirGNNs very much resemble relational GNNs (R-GCN, CompGCN, NBFNet, and so on) who have learnable aggregations per unique relation type (and its inverse), and we recently published a theory paper on the expressiveness of such GNNs. Great to see prominent GNN folks joining the relational party 😉 The Learning on Graphs Conference (LoG) is not just one of the coolest venue for Graph ML research - the organizers do care about the community and factor in your feedback. Bastian Rieck and Corinna Coupette summarized the results from the anonymous poll among authors and reviewers in Evaluating the "Learning on Graphs" Conference Experience highlighting what worked (eg, monetary awards for reviewers and lesser paper load) and not quite (some papers were rejected even when authors submitted rebuttals but reviewers did not engage). Let’s help LoG to grow to the best Graph ML research venues! FAIR, CMU, and The Open Catalyst Project are about to announce the next large NeurIPS challenge - most likely it would be about adsorption energy estimation. Brace yourselves and prepare your best equivariant geometrics models. Da Zheng and Florian Saupe from AWS published a post introducing GraphStorm (we noticed the original paper a few weeks ago), a low-code framework for large-scale graph learning targeted for enterprise applications. The post goes through several examples on graph building and running inference. Some weekend reading: Ewald-based Long-Range Message Passing for Molecular Graphs (ICML’23) and its LOG2 reading group presentation Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting - comparison of AlphaFold2 vs ESMFold How does over-squashing affect the power of GNNs? feat. Francesco Di Giovanni, Michael Bronstein, and Petar Veličković A Fractional Graph Laplacian Approach to Oversmoothing feat. Gitta Kutyniok

Graph ML News (June 3rd) Molecular ML conference (MoML) took place in Montreal on Monday and hosted invited talks from Marinka Zitnik, Mohammed AlQuraishi, Gábor Csányi, and other top researchers in the field. The recordings are now online on YouTube. Accepted papers at ICML 2023 are now visible on the conference website (available after registration) - several editors of this channel are going to ICML this year and it is likely we will prepare an overview of the most interesting graph papers! Weekend reading: Geometric Latent Diffusion Models for 3D Molecule Generation (ICML’23) feat. Minkai Xu, Stefano Ermon, and Jure Leskovec Protein Design with Guided Discrete Diffusion feat. Kyunghyun Cho and Andrew Wilson Graph Inductive Biases in Transformers without Message Passing (ICML’23) feat. Derek Lim Unsupervised Embedding Quality Evaluation feat. Anton Tsitsulin Smooth, exact rotational symmetrization for deep learning on point clouds

Graph ML News (May 27th): New Antibiotic found with Geometric DL, Differential Privacy, NeurIPS Submissions A new antibiotic abaucin is discovered by the power of Geometric Deep Learning! Abaucin targets a stubborn Acinetobacter baumannii pathogen resistant to many drugs. The new Nature Chem Bio paper (feat. Regina Barzilay and Tommi Jaakkola from MIT) sheds more light on the screening process and used methods. Stanford launches the online version of the flagship CS224W course of Graph ML. The 10-credit course is priced at $1,750 and starts on June 5th. The TAG in ML workshop on topology announced a new challenge: implementing more topology-enabled neural nets with the TopoModelX framework where top contributors will become co-authors of a JMLR submission. That’s a great option for those who’d like to start working with topological neural architectures! Vincent Cohen-Addad and Alessandro Epasto of Google Research published a post on differentiably-private clustering: introducing an approach for DP hierarchical clustering with formal guarantees and lower bounds, and an approach for large-scale DP clustering. The Weekend Reading section this week is brought to you by NeurIPS submissions, quite a number of cool papers: Link Prediction for Flow-Driven Spatial Networks - the work introduces the Graph Attentive Vectors (GAV) framework for link prediction (based on the labeling trick commonly used in LP) and smashes the OGB-Vessel leaderboard with a 10-points rocauc margin to the previous SOTA. Edge Directionality Improves Learning on Heterophilic Graphs feat. Emanuele Rossi, Francesco Di Giovanni, Fabrizio Frasca, Michael Bronstein, and Stephan Günnemann PRODIGY: Enabling In-context Learning Over Graphs feat. Qian Huang, Hongyu Ren, Percy Liang, and Jure Leskovec - a cool attempt to bring prompting to the permutation-invariant nature of graphs. Uncertainty Quantification over Graph with Conformalized Graph Neural Networks feat. Kexin Huang and Jure Leskovec — one of the first works on Conformal Prediction with GNNs. Learning Large Graph Property Prediction via Graph Segment Training feat. Jure Leskovec and Bryan Perozzi ChatDrug - a neat attempt at combining ChatGPT with retrieval plugins and molecular models to edit molecules, peptides, and proteins right with natural language. Extension of MoleculeSTM that we featured in the recent State of Affairs post. MISATO - Machine learning dataset for structure-based drug discovery - a new dataset of 20K protein-ligand complexes with molecular dynamics traces and electronic properties. Multi-State RNA Design with Geometric Multi-Graph Neural Networks feat. Chaitanya Joshi and Pietro Lio

Graph ML News (May 20th) The NeurIPS deadline has passed so we could finally disconnect from the cluster, breathe in some fresh air and get ready for the supplementary deadline and/or paper bidding depending on your status. The Workshop on Mining and Learning with Graph (MLG) at KDD’23 accepts submissions until May 30th. This year KDD will feature both MLG and Graph Learning Benchmarks (GLB), so two more reasons to visit Long Beach and chat with the fellow graph folks 😉 CS224W, one of the best graph courses from Stanford, started publishing project reports of the Winter 2023 cohort: some new articles include solving TSP with GNNs, approaching code similarity, and building music recommendation system with GNNs. More reports will be published within the next few weeks. Weekend reading: DRew: Dynamically Rewired Message Passing with Delay feat. Michael Bronstein and Francesco Di Giovanni (ICML’23) Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs (ICML’23) Can Language Models Solve Graph Problems in Natural Language? On the Connection Between MPNN and Graph Transformer

Academic opportunities in Geometric DL A new batch of open positions (apparently when the funding got finally secured for the next financial year 🙂 ) - keep in mind that some of them accept applications only for the next few weeks. Internship at the AI4Science PDE Team at Microsoft Research with Johannes Brandstetter, Cristian Bodnar, and Max Welling Postdoc in ML and Systems Biologywith Karsten Borgwardt at MPI Munich PhD Student in Generative Models for Inverse Molecular Design with Simon Olsson at Chalmers University of Technology Faculty and PhD positions on Foundation Models for Science with Miles Cranmer, Shirley Ho, Kyunghyun Cho at the Simons Foundation and Flatiron Institute