Network vertices are often divided into groups or communities with dense connections within
communities and sparse connections between communities. Community detection has …

Finding community structures in social networks is considered to be a challenging task as
many of the proposed algorithms are computationally expensive and does not scale well for …

Community detection is omnipresent in all complex network analyses. It consists of dividing
networks into a set of nodes that are highly interconnected. These groups of nodes allow a …

The community detection in networks is a prominent task in the graph data mining, because
of the rapid emergence of the graph data; eg, information networks or social networks. In this …

Community detection aims to discover cohesive groups in which people connect with each
other closely in social networks. A variety of methods have been proposed to detect …

Graph vertices are often divided into groups or communities with dense connections within
communities and sparse connections between communities. Community detection has …

Detecting communities or clusters of networks is a considerable interesting problem in
various fields and interdisciplinary subjects in recent years. Tens of hundreds of methods …

Many datasets can be described in the form of graphs or networks where nodes in the graph
represent entities and edges represent relationships between pairs of entities. A common …

Community detection in networks is used to understand the underlying structure of the
network and obtain insight into the structure of the network. Evaluation of the detected …

Community detection is an important task for identifying the structure and function of
complex networks. The task is challenging as communities often show overlapping and …