Real world complex networks are indirect representation of complex systems. They grow
over time. These networks are fragmented and raucous in practice. An important concern …

Link prediction, which aims to predict future or missing links among nodes, is a crucial
research problem in social network analysis. A unique few-shot challenge is link prediction …

With the increase in municipal solid waste (MSW), most cities face solid waste management
issues. In this study, the analytic hierarchy process (AHP) and artificial neural network (ANN) …

Finding communities in complex networks is a challenging task and one promising
approach is the Stochastic Block Model (SBM). But the influences from various fields led to a …

A Non-negative Matrix Factorization (NMF)-based method is proposed to solve the link
prediction problem in dynamic graphs. The method learns latent features from the temporal …

Feature selection (FS) plays an important role in data mining and recognition, especially
regarding large scale text, images and biological data. The Markov blanket provides a …

In complex networks, the existing link prediction methods primarily focus on the internal
structural information derived from single-layer networks. However, the role of interlayer …

To fulfill the missing part in cascading failures based reliability study of bus transit network
(BTN), this paper is devoted to establishing a novel cascading failures model under route …

Modeling complex systems as networks of interacting elements has gained increased
attention in recent years. So far, network modeling in manufacturing and logistics has often …

Network pharmacology elucidates the relationship between drugs and targets. As the
identified targets for each drug increases, the corresponding drug–target network (DTN) …