Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node is updated by aggregating the embeddings of its neighbors. However, not all information aggregated from neighbors is beneficial. In some cases, a portion of the neighbor information may be harmful to the downstream tasks. For the high-quality aggregation of beneficial information, we propose a flexible method EGAI (Enhancing Graph neural networks by a high-quality Aggregation of beneficial Information). The core concept of this method is to filter out the redundant and harmful information by re...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applic...
Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irreg...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Attributed graph embedding aims to learn node representation based on the graph topology and node at...
Mining from graph-structured data is an integral component of graph data management. A recent trendi...
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass ...
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured...
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object c...
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural an...
Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an im...
Data augmentation has been widely used to improve generalizability of machine learning models. Howe...
Graphs are important data structures that can capture interactions between individual entities. The...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applic...
Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irreg...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Attributed graph embedding aims to learn node representation based on the graph topology and node at...
Mining from graph-structured data is an integral component of graph data management. A recent trendi...
Graph Neural Networks (GNNs) is an architecture for structural data, and has been adopted in a mass ...
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured...
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object c...
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural an...
Learning graph-structured data with graph neural networks (GNNs) has been recently emerging as an im...
Data augmentation has been widely used to improve generalizability of machine learning models. Howe...
Graphs are important data structures that can capture interactions between individual entities. The...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
One of the most predominant techniques that have achieved phenomenal success in many modern applicat...
There has been a rising interest in graph neural networks (GNNs) for representation learning over th...
International audienceGraph Neural Networks (GNNs) have succeeded in various computer science applic...