Publications (100)
Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan +2
The 2021 RecSys Challenge Dataset: Fairness is not optional
Luca Belli, Alykhan Tejani, Frank Portman +10
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Cristian Bodnar, Fabrizio Frasca, Yu Guang Wang +4
Graph signal processing for machine learning: A review and new perspectives
Xiaowen Dong, Dorina Thanou, Laura Toni +2
Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity
Yam Eitan, Yoav Gelberg, Guy Bar-Shalom +3
Edge Directionality Improves Learning on Heterophilic Graphs
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni +3
LLMs can hide text in other text of the same length
Antonio Norelli, Michael Bronstein
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch +7
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
Weisfeiler and Lehman Go Cellular: CW Networks
Cristian Bodnar, Fabrizio Frasca, Nina Otter +4
Cooperative Graph Neural Networks
Ben Finkelshtein, Xingyue Huang, Michael Bronstein +1
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov, Cheng-Hao Liu, Moksh Jain +31
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova +3
Heterogeneous manifolds for curvature-aware graph embedding
Francesco Di Giovanni, Giulia Luise, Michael Bronstein
SIGN: Scalable Inception Graph Neural Networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard +3
Robust Principal Component Analysis on Graphs
Nauman Shahid, Vassilis Kalofolias, Xavier Bresson +2
Flow-Based Fragment Identification via Binding Site-Specific Latent Representations
Rebecca Manuela Neeser, Ilia Igashov, Arne Schneuing +3
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi +2
GraphText: Graph Reasoning in Text Space
Jianan Zhao, Le Zhuo, Yikang Shen +5
Understanding Virtual Nodes: Oversquashing and Node Heterogeneity
Joshua Southern, Francesco Di Giovanni, Michael Bronstein +1
Temporal Graph Networks for Deep Learning on Dynamic Graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca +3
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper KapuÅniak, Peter Potaptchik, Teodora Reu +5
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, Ä°smail Ä°lkan Ceylan +1
Efficient Regression-Based Training of Normalizing Flows for Boltzmann Generators
Danyal Rehman, Oscar Davis, Jiarui Lu +5
Attention Sinks and Compression Valleys in LLMs are Two Sides of the Same Coin
Enrique Queipo-de-Llano, Ãlvaro Arroyo, Federico Barbero +4
DRew: Dynamically Rewired Message Passing with Delay
Benjamin Gutteridge, Xiaowen Dong, Michael Bronstein +1
OpenQDC: Open Quantum Data Commons
Cristian Gabellini, Nikhil Shenoy, Stephan Thaler +5
Curvature Filtrations for Graph Generative Model Evaluation
Joshua Southern, Jeremy Wayland, Michael Bronstein +1
On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero +3
RetroBridge: Modeling Retrosynthesis with Markov Bridges
Ilia Igashov, Arne Schneuing, Marwin Segler +2
Can strong structural encoding reduce the importance of Message Passing?
Floor Eijkelboom, Erik Bekkers, Michael Bronstein +1
Training Transformers for KV Cache Compressibility
Yoav Gelberg, Yam Eitan, Michael Bronstein +2
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Ilia Igashov, Hannes Stärk, Clément Vignac +5
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Position: Topological Deep Learning is the New Frontier for Relational Learning
Theodore Papamarkou, Tolga Birdal, Michael Bronstein +19
On the Impact of Sample Size in Reconstructing Graph Signals
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Claudio Battiloro, Indro Spinelli, Lev Telyatnikov +3
Hyperbolic Deep Reinforcement Learning
Edoardo Cetin, Benjamin Chamberlain, Michael Bronstein +1
Mathematical Foundations of Geometric Deep Learning
Haitz Sáez de Ocáriz Borde, Michael Bronstein
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Efficient Deformable Shape Correspondence via Kernel Matching
Zorah Lähner, Matthias Vestner, Amit Boyarski +8
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Guillaume Huguet, James Vuckovic, Kilian Fatras +9
Relaxed Equivariance via Multitask Learning
Ahmed A. Elhag, T. Konstantin Rusch, Francesco Di Giovanni +1
Neural Spacetimes for DAG Representation Learning
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law +2
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties
Soha Sadat Mahdi, Nele Nauwelaers, Philip Joris +8
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina PetroviÄ, Lazar Atanackovic, Viggo Moro +5
Graph Kernel Neural Networks
Luca Cosmo, Giorgia Minello, Alessandro Bicciato +4
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
Zifeng Ding, Shenyang Huang, Zeyu Cao +11
Unsupervised Diffeomorphic Surface Registration and Non-Linear Modelling
Balder Croquet, Daan Christiaens, Seth M. Weinberg +3
MeshGAN: Non-linear 3D Morphable Models of Faces
Shiyang Cheng, Michael Bronstein, Yuxiang Zhou +3
Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Adrian Hayler, Xingyue Huang, Ä°smail Ä°lkan Ceylan +2
Matrix Completion on Graphs
Vassilis Kalofolias, Xavier Bresson, Michael Bronstein +1
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
Kacper KapuÅniak, Cristian Gabellini, Michael Bronstein +2
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Structure-based Drug Design with Equivariant Diffusion Models
Arne Schneuing, Charles Harris, Yuanqi Du +10
Evidence of social learning across symbolic cultural barriers in sperm whales
António Leitão, Maxime Lucas, Simone Poetto +5
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization
Mirco Mutti, Riccardo De Santi, Emanuele Rossi +3
Planner Aware Path Learning in Diffusion Language Models Training
Fred Zhangzhi Peng, Zachary Bezemek, Jarrid Rector-Brooks +5
ncRNA Classification with Graph Convolutional Networks
Emanuele Rossi, Federico Monti, Michael Bronstein +1
Gradient Variance Reveals Failure Modes in Flow-Based Generative Models
Teodora Reu, Sixtine Dromigny, Michael Bronstein +1
TGM: a Modular and Efficient Library for Machine Learning on Temporal Graphs
Jacob Chmura, Shenyang Huang, Tran Gia Bao Ngo +7
Non-Rigid Puzzles
Or Litany, Emanuele RodolÃ, Alex Bronstein +2
MacroGuide: Topological Guidance for Macrocycle Generation
Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein +3
Balancing Efficiency and Expressiveness: Subgraph GNNs with Walk-Based Centrality
Joshua Southern, Yam Eitan, Guy Bar-Shalom +3
Locality-Aware Graph-Rewiring in GNNs
Federico Barbero, Ameya Velingker, Amin Saberi +2
Single Image 3D Hand Reconstruction with Mesh Convolutions
Dominik Kulon, Haoyang Wang, Riza Alp Güler +2
Deformable Shape Completion with Graph Convolutional Autoencoders
Or Litany, Alex Bronstein, Michael Bronstein +1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, Ä°smail Ä°lkan Ceylan, Michael Bronstein +1
Privacy-Aware Recommender Systems Challenge on Twitter's Home Timeline
Luca Belli, Sofia Ira Ktena, Alykhan Tejani +12
MUX: Continuous Reasoning via Multiplexed Tokens
Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein +2
Categorical Flow Maps
Daan Roos, Oscar Davis, Floor Eijkelboom +5
Why do LLMs attend to the first token?
Federico Barbero, Ãlvaro Arroyo, Xiangming Gu +4
Can Graph Foundation Models Generalize Over Architecture?
Benjamin Gutteridge, Michael Bronstein, Xiaowen Dong
DyGMamba: Efficiently Modeling Long-Term Temporal Dependency on Continuous-Time Dynamic Graphs with State Space Models
Zifeng Ding, Yifeng Li, Yuan He +5
GradMetaNet: An Equivariant Architecture for Learning on Gradients
Yoav Gelberg, Yam Eitan, Aviv Navon +5
Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation
Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis +2
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning
Ãlvaro Arroyo, Alessio Gravina, Benjamin Gutteridge +5
Supercharging Graph Transformers with Advective Diffusion
Qitian Wu, Chenxiao Yang, Kaipeng Zeng +1
Sheaf Neural Networks with Connection Laplacians
Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde +3
Latent-Graph Learning for Disease Prediction
Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi +2
Are Large Language Models Good Temporal Graph Learners?
Shenyang Huang, Ali Parviz, Emma Kondrup +5
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein
Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao, Emily Jin, Michael Bronstein +2
OXtal: An All-Atom Diffusion Model for Organic Crystal Structure Prediction
Emily Jin, Andrei Cristian Nica, Mikhail Galkin +8
On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
Guadalupe Gonzalez, Shunwang Gong, Ivan Laponogov +2
Multi-domain Distribution Learning for De Novo Drug Design
Arne Schneuing, Ilia Igashov, Adrian W. Dobbelstein +3
SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operator
Shunwang Gong, Lei Chen, Michael Bronstein +1
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet +7
Riemannian Metric Matching for Scalable Geometric Modeling of Distributions
Jacob Bamberger, Adam Gosztolai, Pierre Vandergheynst +2
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
Dominik Kulon, Riza Alp Güler, Iasonas Kokkinos +2
Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction
Jarrid Rector-Brooks, Mohsin Hasan, Zhangzhi Peng +9
Graph Neural Networks Are Not Continuous Across Graph Resolutions
Christian Koke, Yuesong Shen, Abhishek Saroha +4
Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein, Ä°smail Ä°lkan Ceylan, Michael Bronstein +1
Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios +2
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Jonathan Kouchly, Ben Finkelshtein, Michael Bronstein +1