Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

Graph Convolutional Networks (GCNs) are an emerging type of neural network model on
graphs which have achieved state-of-the-art performance in the task of node classification …

Graph representation learning has re-emerged as a fascinating research topic due to the
successful application of graph convolutional networks (GCNs) for graphs and inspires …

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes
that capture various relationships in a multi-view network, where each view represents a …

Machine learning on graphs has been extensively studied in both academic and industry.
However, as the literature on graph learning booms with a vast number of emerging …

Abstract Discovering ideal Graph Neural Networks (GNNs) architectures for different tasks is
labor intensive and time consuming. To save human efforts, Neural Architecture Search …

Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Graph neural architecture search (GraphNAS) has recently aroused considerable attention
in both academia and industry. However, two key challenges seriously hinder the further …