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 detection is a growing field of interest in the area of social network applications.
Many community detection methods and surveys have been introduced in recent years, with …

The paper addresses clustering problems from combinatorial viewpoints. A systemic survey
is presented. The list of considered issues involves the following:(1) literature analysis of …

Identifying communities or clusters in networked systems has received much attention
across the physical and social sciences. Most of this work focuses on single layer or one …

Leader-driven community detection algorithms (LdCD hereafter) constitute a new trend in
devising algorithms for community detection in large-scale complex networks. The basic …

There has been a surge of interest in community detection in homogeneous single-relational
networks which contain only one type of nodes and edges. However, many real-world …

In this work we propose a new efficient algorithm for communities construction based on the
idea that a community is animated by a set of leaders that are followed by a set of nodes. A …

Complex networks contain various interactions among similar or different entities. These
kinds of networks are called multi-relational networks, in which each layer corresponds to a …

We combine bi-and triclustering to analyse data collected from the Russian online social
network Vkontakte. Using biclustering we extract groups of users with similar interests and …

Abstract Objects that are interrelated with each other are often represented as
homogeneous networks, in which objects are of the same entity type and relationships …