Convolutional neural networks (CNNs) have received widespread attention due to their
powerful modeling capabilities and have been successfully applied in natural language …

Graph learning methods have recently been receiving increasing interest as means to infer
structure in datasets. Most of the recent approaches focus on different relationships between …

Understanding the organizational principles of human brain activity at the systems level
remains a major challenge in network neuroscience. Here, we introduce a fully data-driven …

Structure inference is an important task for network data processing and analysis in data
science. In recent years, quite a few approaches have been developed to learn the graph …

We propose an approach for estimating graph diffusion processes using annihilation filters
from a finite set of observations of the diffusion process made at regular intervals. Our …

Graphs offer a simple yet meaningful representation of relationships between data. This
representation is often used in machine learning algorithms in order to incorporate structural …