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 …

Modelling the human brain as a complex network has provided a powerful mathematical
framework to characterize the structural and functional architectures of the brain. In the past …

Many community detection algorithms have been developed to uncover the mesoscopic
properties of complex networks. However how good an algorithm is, in terms of accuracy …

In most natural and engineered systems, a set of entities interact with each other in
complicated patterns that can encompass multiple types of relationships, change in time and …

A social network as an essential communication platform facilitates the interactions of online
users. Based on the interactions, users can influence or be affected by the opinions of …

Detecting and analyzing dense groups or communities from social and information networks
has attracted immense attention over the last decade due to its enormous applicability in …

Community structure is one of the main structural features of networks, revealing both their
internal organization and the similarity of their elementary units. Despite the large variety of …

Growth and remodelling impact the network topology of complex systems, yet a general
theory explaining how new links arise between existing nodes has been lacking and little is …

Until recently, understanding the regulatory behavior of cells has been pursued through
independent analysis of the transcriptome or the proteome. Based on the central dogma, it …

Influential nodes play a critical role in boosting or curbing spreading phenomena in complex
networks. Numerous centrality measures have been proposed for identifying and ranking …