Link prediction in complex networks is a critical process aimed at uncovering hidden or
potential connections among nodes. This technique is widely utilized in areas such as …

Attributed network embedding (ANE) maps nodes in a network into a low-dimensional space
while preserving the intrinsic essence of node attribute and network topology. Incorporating …

Attributed network embedding enables joint representation learning of node links and
attributes. Existing attributed network embedding models are designed in continuous …

Learning node embedding for graphs has been proved essential for a wide range of
applications, from recommendation to community search. However, most existing …

Almost all the existing network embedding methods learn to map the node IDs to their
corresponding node embeddings. This design principle, however, hinders the existing …

Link prediction (LP) plays a crucial role in network science, which forecasts potential
connections or relationships between nodes or entities within the network. Link prediction …

The continuous development of wireless communication networks makes the interaction
between users become frequent, and the analysis of network users has become a hot topic …

Network embedding has attracted a surge of attention recently. In this field, how to preserve
high-order proximity has long been a difficult task. Graph convolutional network (GCN) and …

Link prediction is an important task in social network analysis and mining because of its
various applications. A large number of link prediction methods have been proposed …

Dynamic link prediction aims to predict future connections among unconnected nodes in a
network. It can be applied for friend recommendations, link completion, and other tasks …