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 …

Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

Network alignment is a critical steppingstone behind a variety of multi-network mining tasks.
Most of the existing methods essentially optimize a Frobenius-like distance or ranking-based …

Graph Convolutional Network (GCN) has exhibited strong empirical performance in many
real-world applications. The vast majority of existing works on GCN primarily focus on the …

Graph structures have attracted much research attention for carrying complex relational
information. Based on graphs, many algorithms and tools are proposed and developed for …

Multi-layered inter-dependent networks have emerged in a wealth of high-impact application
domains. Cross-layer dependency inference, which aims to predict the dependencies …

Graph alignment, which aims at identifying corresponding entities across multiple networks,
has been widely applied in various domains. As the graphs to be aligned are usually …

Personalized PageRank, as a graphical model, has been proven as an effective solution in
many applications such as web page search, recommendation, etc. However, in the real …

Graph anomaly detection aims to identify the atypical substructures and has attracted an
increasing amount of research attention due to its profound impacts in a variety of …