Add to ORKGWe propose a new Graph Neural Network that combines re-cent advancements in the field. We give theoretical contributions by prov-ing that the model is strictly more general than the Graph IsomorphismNetwork and the Gated Graph Neural Network, as it can approximate thesame functions and deal with arbitrary edge values. Then, we show howa single node information can flow through the graph unchange
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Deep learning for graphs encompasses all those neural models endowed with multiple layers of comput...
Graph Neural Networks (GNNs) have achieved enormous success in tackling analytical problems on graph...
We propose a new Graph Neural Network that combines re-cent advancements in the field. We give theo...
In several applications the information is naturally represented by graphs. Traditional approaches c...
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent ...
In this paper, we present a new neural network model, called graph neural network model, which is a ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edg...
In this paper, we will consider the universal approximation properties of a recently introduced neur...
Many NLP applications can be framed as a graph-to-sequence learning problem.Previous work proposing ...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Graphs are a natural choice to encode data in many real–world applications. In fact, a graph can des...
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achie...
Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks wh...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Deep learning for graphs encompasses all those neural models endowed with multiple layers of comput...
Graph Neural Networks (GNNs) have achieved enormous success in tackling analytical problems on graph...
We propose a new Graph Neural Network that combines re-cent advancements in the field. We give theo...
In several applications the information is naturally represented by graphs. Traditional approaches c...
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent ...
In this paper, we present a new neural network model, called graph neural network model, which is a ...
The theme of this dissertation is machine learning on graph data. Graphs are generic models of signa...
Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edg...
In this paper, we will consider the universal approximation properties of a recently introduced neur...
Many NLP applications can be framed as a graph-to-sequence learning problem.Previous work proposing ...
Structures or graphs are pervasive in our lives. Although deep learning has achieved tremendous succ...
Graphs are a natural choice to encode data in many real–world applications. In fact, a graph can des...
Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achie...
Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks wh...
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs ...
Deep learning for graphs encompasses all those neural models endowed with multiple layers of comput...
Graph Neural Networks (GNNs) have achieved enormous success in tackling analytical problems on graph...