Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new
architectures as well as novel applications. However, current research focuses on proposing …

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then
introduced into the optimization community. BLO is able to handle problems with a …

We study the problem of semi-supervised learning on graphs, for which graph neural
networks (GNNs) have been extensively explored. However, most existing GNNs inherently …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

Graph neural networks (GNNs) have been widely used in various graph analysis tasks. As
the graph characteristics vary significantly in real-world systems, given a specific scenario …

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 …

Spatio-temporal graphs are important structures to describe urban sensory data, eg, traffic
speed and air quality. Predicting over spatio-temporal graphs enables many essential …

Abstract While Graph Neural Networks (GNNs) recently became powerful tools in graph
learning tasks, considerable efforts have been spent on improving GNNs' structural …