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 …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

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 pre-training strategies have been attracting a surge of attention in the graph mining
community, due to their flexibility in parameterizing graph neural networks (GNNs) without …

Graph self-supervised learning has attracted plenty of attention in recent years. However,
most existing methods are designed for homogeneous graphs yet not tailored for bipartite …

Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …

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 …