Add to ORKGRecent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, logistics). However, implementing a GNN prototype is still a cumbersome task that requires strong skills in neural network programming. This poses an important barrier to researchers and practitioners that want to apply GNN to their specific problems but do not have the needed Machine Learning expertise. In this paper, we present IGNNITION, a novel open-source framework for fast prototyping of GNNs. This framework is built on top of TensorFlow, and offers an intuitive high-level abstraction that allows the user to define its GNN model via a YAML file, being completely oblivious to the tensor-wise opera...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
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...
Graphs are a fundamental data type that enables to represent in a well-structured manner many object...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designe...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicabilit...
There are plenty of graph neural network (GNN) accelerators being proposed. However, they highly rel...
This thesis is also part of a bigger project that is composed of 2 other final degree thesis. The mo...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
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...
Graphs are a fundamental data type that enables to represent in a well-structured manner many object...
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to ...
TensorFlow GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designe...
Graph neural networks (GNNs) have received great attention due to their success in various graph-rel...
Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicabilit...
There are plenty of graph neural network (GNN) accelerators being proposed. However, they highly rel...
This thesis is also part of a bigger project that is composed of 2 other final degree thesis. The mo...
In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing a...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...
Despite advances in the field of Graph Neural Networks (GNNs), only a small number (~5) of datasets ...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the premise of ...
Recent advancements in Deep Learning (DL) have revolutionized the way we can efficiently tackle comp...