Networks (or graphs) appear as dominant structures in diverse domains, including
sociology, biology, neuroscience and computer science. In most of the aforementioned …

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 …

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 …

Detecting community structure is fundamental for uncovering the links between structure and
function in complex networks and for practical applications in many disciplines such as …

This chapter characterises the scope of this book. It explains the reasons why one should be
interested in cluster analysis, lists major application areas, basic theoretical and practical …

Starting from a general ansatz, we show how community detection can be interpreted as
finding the ground state of an infinite range spin glass. Our approach applies to weighted …

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

In this paper, we first discuss the definition of modularity (Q) used as a metric for community
quality and then we review the modularity maximization approaches which were used for …

The complexity of biological, social, and engineering networks makes it desirable to find
natural partitions into clusters (or communities) that can provide insight into the structure of …

We propose a quantitative function for community partition—ie, modularity density or D
value. We demonstrate that this quantitative function is superior to the widely used …