The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Network embedding assigns nodes in a network to low-dimensional representations and
effectively preserves the network structure. Recently, a significant amount of progresses …

The explosive growth in Big Data has attracted much attention in designing efficient indexing
and search methods recently. In many critical applications such as large-scale search and …

Network embedding has recently attracted lots of attentions in data mining. Existing network
embedding methods mainly focus on networks with pairwise relationships. In real world …

Abstract Knowledge graph (KG for short) alignment aims at building a complete KG by
linking the shared entities across complementary KGs. Existing approaches assume that …

Abstract Singular Value Decomposition (SVD) is a popular approach in various network
applications, such as link prediction and network parameter characterization. Incremental …

Despite significant progress in supervised hashing, there are three common limitations of
existing methods. First, most pioneer methods discretely learn hash codes bit by bit, making …

Background Recent studies have proposed deep learning techniques, namely recurrent
neural networks, to improve biomedical text mining tasks. However, these techniques rarely …

Network embedding algorithms to date are primarily designed for static networks, where all
nodes are known before learning. How to infer embeddings for out-of-sample nodes, ie …

In recent years, with the advancement of wireless communication and location acquisition
technology in the context of modern smart cities, and the increasing popularity of mobile …