The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

In this paper, we propose an end-to-end graph learning framework, namely\textbf {I}
terative\textbf {D} eep\textbf {G} raph\textbf {L} earning (\alg), for jointly and iteratively …

Graph-based semi-supervised learning (GSSL) serves as a powerful tool to model the
underlying manifold structures of samples in high-dimensional spaces. It involves two …

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative
and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph …

Recently, graph neural networks (GNNs) have been widely used for graph representation
learning, where the central idea is to recursively aggregate neighborhood information to …

Graph neural networks (GNNs) have been increasingly deployed in various applications that
involve learning on non-Euclidean data. However, recent studies show that GNNs are …

With the advancement of brain imaging techniques and a variety of machine learning
methods, significant progress has been made in brain disorder diagnosis, in particular …

Abstract Multiple Instance Learning (MIL) is a weakly supervised learning problem where
the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised …

Network-topology inference from (vertex) signal observations is a prominent problem across
data-science and engineering disciplines. Most existing schemes assume that observations …