This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

Graph convolutional neural networks (GCNNs) are nonlinear processing tools to learn
representations from network data. A key property of GCNNs is their stability to graph …

Graph Neural Networks (GNN) rely on graph convolutions to learn features from network
data. GNNs are stable to different types of perturbations of the underlying graph, a property …

Convolutional Neural Networks (CNNs) have been applied to data with underlying non-
Euclidean structures and have achieved impressive successes. This brings the stability …

We present a new approach for learning unsupervised node representations in community
graphs. We significantly extend the Interferometric Graph Transform (IGT) to community …

Graph neural networks (GNNs) use graph convolutions to exploit network invariances and
learn meaningful features from network data. However, on large-scale graphs convolutions …

Abstract Graph Neural Network holds significant importance in various applications.
Pioneering research has demonstrated state-of-the-art performance in practical applications …

This work develops\emph {mixup to graph data}. Mixup has shown superiority in improving
the generalization and robustness of neural networks by interpolating features and labels of …