4 citations · 10 across the 17 of their papers we have counts for
18 papers
MUX: Continuous Reasoning via Multiplexed Tokens
Ayhan Suleymanzade, Halil Alperen Gozeten, Michael Bronstein +2
Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is computationally bottlenecked: each reasoni…
MacroGuide: Topological Guidance for Macrocycle Generation
Alicja Maksymiuk, Alexandre Duplessis, Michael Bronstein +3
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets. D…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina Petrović, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
Bringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Adrian Hayler, Xingyue Huang, İsmail İlkan Ceylan +2
Graph foundation models (GFMs) have recently emerged as a promising paradigm for achieving broad generalization across various graph data. However, existing GFMs are often trained…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Graph machine learning architectures are typically tailored to specific tasks on specific datasets, which hinders their broader applicability. This has led to a new quest in graph…
HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs
Xingyue Huang, Mikhail Galkin, Michael M. Bronstein +1
Inductive link prediction with knowledge hypergraphs is the task of predicting missing hyperedges involving completely novel entities (i.e., nodes unseen during training). Existing…
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Jonathan Kouchly, Ben Finkelshtein, Michael Bronstein +1
Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the n…
How Expressive are Knowledge Graph Foundation Models?
Xingyue Huang, Pablo Barceló, Michael M. Bronstein +4
Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep learning on knowledge graphs (KGs), as they can generalize to completely novel knowledge graphs with differen…
Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao, Emily Jin, Michael Bronstein +2
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on gr…
Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from t…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured mo…
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, İsmail İlkan Ceylan +1
A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power. Their inability…
Link Prediction with Relational Hypergraphs
Xingyue Huang, Miguel Romero Orth, Pablo Barceló +2
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful appli…
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of discip…
GraphText: Graph Reasoning in Text Space
Jianan Zhao, Le Zhuo, Yikang Shen +5
Large Language Models (LLMs) have gained the ability to assimilate human knowledge and facilitate natural language interactions with both humans and other LLMs. However, despite th…
Cooperative Graph Neural Networks
Ben Finkelshtein, Xingyue Huang, Michael Bronstein +1
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant t…