Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …

Complex networks have been used widely to model a large number of relationships. The
outbreak of COVID-19 has had a huge impact on various complex networks in the real …

Link prediction is one of the most widely studied problems in the area of complex network
analysis, in which machine learning techniques can be applied to deal with it. The biggest …

Networks are now more crucial than ever for modeling complex systems with interconnected
components. Many methods have been developed to infer unobserved links or predict latent …

In drug development, unexpected side effects are the main reason for the failure of
candidate drug trials. Discovering potential side effects of drugs in silico can improve the …

Complex systems widely exist in nature and human society. There are complex interactions
between system elements in a complex system, and systems show complex features at the …

Link prediction is a fundamental research problem in network data analysis. Networks
usually contain rich node-to-node topological metrics and their effective use is crucial to …

Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in
recent years in both the pharmaceutical industry and research community. It refers to the …

Identifying the unintended effects of drugs (side effects) is a very important issue in
pharmacological studies. The laboratory verification of associations between drugs and side …

Link prediction aims to predict missing links or eliminate spurious links and anticipate new
links by analyzing observed network topological structure information. Non-negative matrix …