This paper presents a comprehensive study on clustering: exiting methods and
developments made at various times. Clustering is defined as an unsupervised learning …

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain
networks, from molecular to behavioral scales, are ever increasing in size and complexity …

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease
(AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples …

Improved understanding of human-nature interactions is crucial to conservation science and
practice, but collecting relevant data remains challenging. Recently, social media have …

Multilayer networks is a rising topic in Network Science which characterizes the structure
and the function of complex systems formed by several interacting networks. Multilayer …

Community detection in networks is one of the most popular topics of modern network
science. Communities, or clusters, are usually groups of vertices having higher probability of …

Traditional link prediction methods are generally only calculated for the neighbor information
of nodes, and the network path between nodes has not been fully utilized. Therefore, this …

The modern science of networks has made significant advancement in the modeling of
complex real-world systems. One of the most important features in these networks is the …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

The development of new technologies for map** structural and functional brain
connectivity has led to the creation of comprehensive network maps of neuronal circuits and …