Based on the dynamics towards synchronization in hierarchical networks, we present an efficient method for extracting hierarchical organization in complex network. In the synchronization process, hierarchical structures corresponding to well defined communities of nodes emerge in different time...

The investigation of community structures is one of the most important problems in the field of complex networks and has countless applications in different disciplines: biology, computer, social sciences, etc. Many community detection algorithms have been developed in various fields recently....

The investigation of community structure in networks is an important issue in many disciplines, which still remains a challenging task. First, complex networks often show a hierarchical structure with communities embedded within other communities. Moreover, communities in the network may overlap...

Community detection is of considerable interest for analyzing the structure and function of complex networks. Recently, a type of multi-resolution methods in community detection was introduced, which can adjust the resolution of modularity by modifying the modularity function with tunable...

Community detection is one of the most investigated problems in the field of complex networks. Although several methods were proposed, there is still no precise definition of communities. As a step toward a definition, I highlight two necessary properties of communities, separation and internal...

Decomposing a network into small modules or communities is beneficial for understanding the structure and dynamics of the network. One of the most prominent approaches is to repeatedly join communities together in pairs with the greatest increase in modularity so that a dendrogram that shows the...

The investigation of link prediction in networks is an important issue in many disciplines. The research of prediction algorithms which required short time but high accuracy is still a challenging task. Most of the existing algorithms are based on the topological information of the networks,...

Most community detection methods use network topology and edge density to identify optimal communities. However, in these methods, several objects that are connected by high weights may be decomposed into different communities, even when they intuitively belong to a single community. In this...

Recent research has shown great interest in statistical inference methods for community detection, not only in models and algorithms but also in the detectability. In this paper we propose a fast community detection algorithm based on the degree-corrected block model. By introducing a parameter...

How to measure the similarity between nodes is of great importance for fuzzy clustering when we use the approach to uncover communities in complex networks. In this paper, we first measure the similarity between nodes in a network based on edge centralities and model the network as a fuzzy...