Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Deep clustering with attribute-missing graphs, where only a subset of nodes possesses
complete attributes while those of others are missing, is an important yet challenging topic in …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

Urban traffic forecasting is the cornerstone of the intelligent transportation system (ITS).
Existing methods focus on spatial-temporal dependency modeling, while two intrinsic …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Abstract While Graph Neural Networks (GNNs) have recently become the de facto standard
for modeling relational data, they impose a strong assumption on the availability of the node …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

Graph neural networks (GNNs) have been widely used in various graph-related problems
such as node classification and graph classification, where the superior performance is …

In recent years, knowledge graph technology has been widely applied in various fields such
as intelligent auditing, urban transportation planning, legal research, and financial analysis …