Network embedding is an effective way to solve the network analytics problems such as
node classification, link prediction, etc. It represents network elements using low …

For networking of big data applications, an essential issue is how to represent networks in
vector space for further mining and analysis tasks, eg, node classification, clustering, link …

Classical link prediction methods mainly utilize vertex information and topological structure
to predict missing links in networks. However, accessing vertex information in real-world …

Network embedding has attracted a surge of attention in recent years. It is to learn the low-
dimensional representation for nodes in a network, which benefits downstream tasks such …

Graph attention networks are a popular method to deal with link prediction tasks, but the
weight assigned to each sample is not focusing on the sample's own performance in …

Network embedding, also known as network representation learning, aims at defining low-
dimensional, continuous vector representation of nodes to maximally preserve the network …

Link prediction, which centers on whether or not a pair of nodes is likely to be connected, is
a fundamental problem in complex network analysis. Network-embedding-based link …

Presently, many users are involved in multiple social networks. Identifying the same user in
different networks, also known as anchor link prediction, becomes an important problem …

Link prediction is critical to completing the missing links in a network or to predicting the
generation of new links according to current network structure information, which is vital for …

Link prediction aims to infer the link existence between pairs of nodes in networks/graphs.
Despite their wide application, the success of traditional link prediction algorithms is …