Add to ORKGGraph neural networks (GNNs) have gained traction over the past few years for their superior performance in numerous machine learning tasks. Graph Convolutional Neural Networks (GCN) are a common variant of GNNs that are known to have high performance in semi-supervised node classification (SSNC), and work well under the assumption of homophily. Recent literature has highlighted that GCNs can achieve strong performance on heterophilous graphs under certain "special conditions". These arguments motivate us to understand why, and how, GCNs learn to perform SSNC. We find a positive correlation between similarity of latent node embeddings of nodes within a class and the performance of a GCN. Our investigation on underlying graph structures of a...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object c...
Graph neural networks take node features and graph structure as input to build representations for n...
Many interesting problems in machine learning are being revisited with new deep learning tools. For ...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2...
Graph convolutional networks (GCN) have achieved promising performance in attributed graph clusterin...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
In recent years, graph neural networks (GNN) have succeeded in many structural data analyses, includ...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks wh...
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph...
Experimental reproducibility and replicability are critical topics in machine learning. Authors hav...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object c...
Graph neural networks take node features and graph structure as input to build representations for n...
Many interesting problems in machine learning are being revisited with new deep learning tools. For ...
Graph Neural Networks (GNNs) have become excessively popular and prominent deep learning techniques ...
Graph neural networks (GNN) have become the default machine learning model for relational datasets, ...
Seminal works on graph neural networks have primarily targeted semi-supervised node classification p...
Recent work has shown that a simple, fast method called Simple Graph Convolution (SGC) (Wu et al., 2...
Graph convolutional networks (GCN) have achieved promising performance in attributed graph clusterin...
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structure...
In recent years, graph neural networks (GNN) have succeeded in many structural data analyses, includ...
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in whi...
Graphs are a ubiquitous data structure that can be exploited in many different problems. In tasks wh...
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph...
Experimental reproducibility and replicability are critical topics in machine learning. Authors hav...
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve...
We present GraphMix, a regularization method for Graph Neural Network based semi-supervised object c...
Graph neural networks take node features and graph structure as input to build representations for n...