Add to ORKGVarious graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the features of neighboring nodes. However, they fail to directly characterize and leverage the structural information. In this paper, we propose multi-duplicated characterization of graph structures using information gain ratio (IGR) for GNNs (MSI-GNN), which enhances the performance of node classification by using an i-hop adjacency matrix as the structural information of the graph data. In MSI-GNN, the i-hop adjacency matrix is adaptively adjusted by two methods: (i) structural features in the matrix are selected based on the IGR, and (ii) the selec...
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification an...
Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured dat...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...
Though graph representation learning (GRL) has made significant progress, it is still a challenge to...
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and se...
Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across nu...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph L...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
Graph Neural Networks (GNNs) have received extensive research attention for their promising performa...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification an...
Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured dat...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...
Though graph representation learning (GRL) has made significant progress, it is still a challenge to...
Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and se...
Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data across nu...
Graph Neural Networks (GNNs) are a promising deep learning approach for circumventing many real-worl...
The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph L...
Graph Neural Networks (GNNs) have achieved great success on a node classification task. Despite the ...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where eac...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
Graph Neural Networks (GNNs) have received extensive research attention for their promising performa...
While many systems have been developed to train Graph Neural Networks (GNNs), efficient model infere...
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification an...
Graph neural networks (GNNs) have achieved great success in many scenarios with graph-structured dat...
We consider the problem of estimating the topology of multiple networks from nodal observations, whe...