Self-contained algorithms to detect communities in networks
Castellano, C, Cecconi, F, Loreto, V, Parisi, D, Radicchi, FEur. Phys. J. B 38, 311 - 319 (2004)
Times cited: 18
Abstract
The investigation of community structures in networks is an important
issue in many domains and disciplines. In this paper we present a new
class of local and fast algorithms which incorporate a quantitative
definition of community. In this way the algorithms for the
identification of the community structure become fully self-contained
and one does not need additional non-topological information in order
to evaluate the accuracy of the results. The new algorithms are tested
on artificial and real-world graphs. In particular we show how the new
algorithms apply to a network of scientific collaborations both in the
unweighted and in the weighted version. Moreover we discuss the
applicability of these algorithms to other non-social networks and we
present preliminary results about the detection of community structures
in networks of interacting proteins.