The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Graphs, such as social networks, word co-occurrence networks, and communication
networks, occur naturally in various real-world applications. Analyzing them yields insight …

Organizing data into sensible grou**s is one of the most fundamental modes of
understanding and learning. As an example, a common scheme of scientific classification …

We consider the problem of detecting communities or modules in networks, groups of
vertices with a higher-than-average density of edges connecting them. Previous work …

A large body of work has been devoted to defining and identifying clusters or communities in
social and information networks, ie, in graphs in which the nodes represent underlying …

This handbook provides a comprehensive and unified account of the main research
developments in cluster analysis. Written by active, distinguished researchers in this area …

Modularity is a recently introduced quality measure for graph clusterings. It has immediately
received considerable attention in several disciplines, particularly in the complex systems …

Recently deep learning has been successfully adopted in many applications such as
speech recognition and image classification. In this work, we explore the possibility of …

Detecting clusters or communities in large real-world graphs such as large social or
information networks is a problem of considerable interest. In practice, one typically chooses …