1 citations · 2 across the 5 of their papers we have counts for
5 papers
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Sergio Rozada, Yiming Qin, Manuel Madeira +2
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an op…
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 such as graph transformers have shown pr…
Learning Efficient Positional Encodings with Graph Neural Networks
Charilaos I. Kanatsoulis, Evelyn Choi, Stephanie Jegelka +2
Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectur…
Generalizability of Graph Neural Networks for Decentralized Unlabeled Motion Planning
Shreyas Muthusamy, Damian Owerko, Charilaos I. Kanatsoulis +2
Unlabeled motion planning involves assigning a set of robots to target locations while ensuring collision avoidance, aiming to minimize the total distance traveled. The problem for…
T-GAE: Transferable Graph Autoencoder for Network Alignment
Jiashu He, Charilaos I. Kanatsoulis, Alejandro Ribeiro
Network alignment is the task of establishing one-to-one correspondences between the nodes of different graphs. Although finding a plethora of applications in high-impact domains,…