Deep semi-supervised learning is a fast-growing field with a range of practical applications.
This paper provides a comprehensive survey on both fundamentals and recent advances in …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Recently, graph neural networks (GNNs) have revolutionized the field of graph
representation learning through effectively learned node embeddings, and achieved state-of …

Neural architecture search (NAS) automatically finds the best task-specific neural network
topology, outperforming many manual architecture designs. However, it can be prohibitively …

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to
construct low rank approximations of the graph Laplacian for graph convolution. Relying on …

Recently, techniques for applying convolutional neural networks to graph-structured data
have emerged. Graph convolutional neural networks (GCNNs) have been used to address …

Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

Graph-based methods have been demonstrated as one of the most effective approaches for
semi-supervised learning, as they can exploit the connectivity patterns between labeled and …

Recently, neural network based approaches have achieved significant progress for solving
large, complex, graph-structured problems. Nevertheless, the advantages of multi-scale …