3 citations · 11 across the 18 of their papers we have counts for
26 papers
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Shervin Khalafi, Igor Krawczuk, Sergio Rozada +3
Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph transformers have recently sh…
RelBench v2: A Large-Scale Benchmark and Repository for Relational Data
Justin Gu, Rishabh Ranjan, Charilaos Kanatsoulis +8
Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relationships across multiple interco…
Graph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
Antonis Vasileiou, Juan Cervino, Pascal Frossard +7
Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine lea…
WindMiL: Equivariant Graph Learning for Wind Loading Prediction
Themistoklis Vargiemezis, Charilaos Kanatsoulis, Catherine Gorlé
Accurate prediction of wind loading on buildings is crucial for structural safety and sustainable design, yet conventional approaches such as wind tunnel testing and large-eddy sim…
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Rishabh Ranjan, Valter Hudovernik, Mark Znidar +7
Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and task…
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures
Vijay Prakash Dwivedi, Charilaos Kanatsoulis, Shenyang Huang +1
Graph machine learning has led to a significant increase in the capabilities of models that learn on arbitrary graph-structured data and has been applied to molecules, social netwo…