Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

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 …

As the scientific enterprise has grown in size and diversity, we need empirical evidence on
the research process to test and apply interventions that make it more efficient and its results …

Networks, also called graphs by mathematicians, provide a useful abstraction of the
structure of many complex systems, ranging from social systems and computer networks to …

Community structure is one of the main structural features of networks, revealing both their
internal organization and the similarity of their elementary units. Despite the large variety of …

We propose and study a set of algorithms for discovering community structure in networks—
natural divisions of network nodes into densely connected subgroups. Our algorithms all …

Inspired by empirical studies of networked systems such as the Internet, social networks,
and biological networks, researchers have in recent years developed a variety of techniques …

Yochai Benkler's The Wealth of Networks is an important contribution to our understanding
of the economic, political, and social forces unleashed by the information revolution and …

Many networks of interest in the sciences, including social networks, computer networks,
and metabolic and regulatory networks, are found to divide naturally into communities or …

Coupled biological and chemical systems, neural networks, social interacting species, the
Internet and the World Wide Web, are only a few examples of systems composed by a large …