229 citations · 553 across the 32 of their papers we have counts for
34 papers
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca +9
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and rele…
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…
SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models
Daniel Levy, Siba Smarak Panigrahi, Sékou-Oumar Kaba +5
Generating novel crystalline materials has the potential to lead to advancements in fields such as electronics, energy storage, and catalysis. The defining characteristic of crysta…
Deconstructing equivariant representations in molecular systems
Kin Long Kelvin Lee, Mikhail Galkin, Santiago Miret
Recent equivariant models have shown significant progress in not just chemical property prediction, but as surrogates for dynamical simulations of molecules and materials. Many of…
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Julia Gastinger, Shenyang Huang, Mikhail Galkin +9
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel model…
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…
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
Hesham Mostafa, Uday Mallappa, Mikhail Galkin +2
The floorplanning of Systems-on-a-Chip (SoCs) and of chip sub-systems is a crucial step in the physical design flow as it determines the optimal shapes and locations of the blocks…
FloorSet -- a VLSI Floorplanning Dataset with Design Constraints of Real-World SoCs
Uday Mallappa, Hesham Mostafa, Mikhail Galkin +2
Floorplanning for systems-on-a-chip (SoCs) and its sub-systems is a crucial and non-trivial step of the physical design flow. It represents a difficult combinatorial optimization p…
A Foundation Model for Zero-shot Logical Query Reasoning
Mikhail Galkin, Jincheng Zhou, Bruno Ribeiro +2
Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple KG completion and aims at answering compositional queries comprised of multiple projections and…
Position: Graph Foundation Models are Already Here
Haitao Mao, Zhikai Chen, Wenzhuo Tang +6
Graph Foundation Models (GFMs) are emerging as a significant research topic in the graph domain, aiming to develop graph models trained on extensive and diverse data to enhance the…
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Yi Ren, Samuel Lavoie, Mikhail Galkin +2
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of resear…
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
Towards Foundation Models for Knowledge Graph Reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa +2
Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of token…
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +3
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Hongyu Ren, Mikhail Galkin, Michael Cochez +2
Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of mu…
Attending to Graph Transformers
Luis Müller, Mikhail Galkin, Christopher Morris +1
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks…
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris +1
Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emer…
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren +1
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural metho…
Using Graph Algorithms to Pretrain Graph Completion Transformers
Jonathan Pilault, Michael Galkin, Bahare Fatemi +3
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. How…
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider, Tim Schopf, Juraj Vladika +3
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a represen…
Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin +4
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.…
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin +4
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalabil…
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi +3
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph…