2 citations · 2 across the 15 of their papers we have counts for
5 papers · 1 filter
Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
Benedict Aaron Tjandra, Federico Barbero, Michael Bronstein
Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dyn…
Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios +2
We propose Scalable Message Passing Neural Networks (SMPNNs) and demonstrate that, by integrating standard convolutional message passing into a Pre-Layer Normalization Transformer-…
Neural Spacetimes for DAG Representation Learning
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law +2
We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as…
On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation
Baskaran Sripathmanathan, Xiaowen Dong, Michael Bronstein
Reconstructing a signal on a graph from noisy observations of a subset of the vertices is a fundamental problem in the field of graph signal processing. This paper investigates how…
Bundle Neural Networks for message diffusion on graphs
Jacob Bamberger, Federico Barbero, Xiaowen Dong +1
The dominant paradigm for learning on graph-structured data is message passing. Despite being a strong inductive bias, the local message passing mechanism suffers from pathological…