Add to ORKGThis project will explore some of the most prominent Graph Neural Network variants and apply them to two tasks: approximation of the community detection Girvan-Newman algorithm and compiled code snippet classification
Graphs are a powerful way to model network data with the objects as nodes and the relationship betwe...
This report investigates various Graph Neural Network (GNN) models and its performance and stability...
In the Graph classification problem, given is a family of graphs and a group of different categories...
This project will explore some of the most prominent Graph Neural Network variants and apply them to...
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...
Many underlying relationships among data in several areas of science and engineering, e.g., computer...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
In several applications the information is naturally represented by graphs. Traditional approaches c...
In several applications the information is naturally represented by graphs. Traditional approaches c...
In several applications the information is naturally represented by graphs. Traditional approaches c...
University of Technology Sydney. Faculty of Engineering and Information Technology.With growing popu...
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edg...
Many real-world entities can be modelled as graphs, such as molecular structures, social networks, o...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
Graphs are a powerful way to model network data with the objects as nodes and the relationship betwe...
This report investigates various Graph Neural Network (GNN) models and its performance and stability...
In the Graph classification problem, given is a family of graphs and a group of different categories...
This project will explore some of the most prominent Graph Neural Network variants and apply them to...
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...
Many underlying relationships among data in several areas of science and engineering, e.g., computer...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
In several applications the information is naturally represented by graphs. Traditional approaches c...
In several applications the information is naturally represented by graphs. Traditional approaches c...
In several applications the information is naturally represented by graphs. Traditional approaches c...
University of Technology Sydney. Faculty of Engineering and Information Technology.With growing popu...
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edg...
Many real-world entities can be modelled as graphs, such as molecular structures, social networks, o...
The human brain’s reasoning is postulated to be done by the creation of graphs from the experiences ...
Graphs are a powerful way to model network data with the objects as nodes and the relationship betwe...
This report investigates various Graph Neural Network (GNN) models and its performance and stability...
In the Graph classification problem, given is a family of graphs and a group of different categories...