Graphs are a fundamental data type that enables to represent in a well-structured manner many objects and problems of real-life; particularly those involving a set of elements that interact in some way with each other (i.e., relational information). Thus, they are widely used in fields where information is mainly relational, such as communication networks, chemistry, physics, biology, or recommendation systems. In order to efficiently build statistical models that can understand and process graph-structured information, a new paradigm of models leveraging Deep Neural Networks arises, called Graph Neural Networks (GNN), with proven efficiency in previous works. The current state of the art of GNN, however, is based on domain-specific archite...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data i...
Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique...
Recent years have seen the vast potential of graph neural networks (GNN) in many fields where data i...
TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designe...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the...
This thesis is also part of a bigger project that is composed of 2 other final degree thesis. The mo...
A graph is an abstract data structure with abundant applications, such as social networks, biochemic...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
Many underlying relationships among data in several areas of science and engineering, e.g., computer...
Relational data present in real world graph representations demands for tools capable to study it ac...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data i...
Graph Neural Networks (GNN) have recently exploded in the Machine Learning area as a novel technique...
Recent years have seen the vast potential of graph neural networks (GNN) in many fields where data i...
TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designe...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Autonomous Fifth Generation (5G) and Beyond 5G (B5G) networks require modelling tools to predict the...
This thesis is also part of a bigger project that is composed of 2 other final degree thesis. The mo...
A graph is an abstract data structure with abundant applications, such as social networks, biochemic...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
Many underlying relationships among data in several areas of science and engineering, e.g., computer...
Relational data present in real world graph representations demands for tools capable to study it ac...
Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundame...
Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially ...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...