582 research outputs found
Stochastic blockmodels and community structure in networks
Stochastic blockmodels have been proposed as a tool for detecting community
structure in networks as well as for generating synthetic networks for use as
benchmarks. Most blockmodels, however, ignore variation in vertex degree,
making them unsuitable for applications to real-world networks, which typically
display broad degree distributions that can significantly distort the results.
Here we demonstrate how the generalization of blockmodels to incorporate this
missing element leads to an improved objective function for community detection
in complex networks. We also propose a heuristic algorithm for community
detection using this objective function or its non-degree-corrected counterpart
and show that the degree-corrected version dramatically outperforms the
uncorrected one in both real-world and synthetic networks.Comment: 11 pages, 3 figure
Different approaches to community detection
A precise definition of what constitutes a community in networks has remained
elusive. Consequently, network scientists have compared community detection
algorithms on benchmark networks with a particular form of community structure
and classified them based on the mathematical techniques they employ. However,
this comparison can be misleading because apparent similarities in their
mathematical machinery can disguise different reasons for why we would want to
employ community detection in the first place. Here we provide a focused review
of these different motivations that underpin community detection. This
problem-driven classification is useful in applied network science, where it is
important to select an appropriate algorithm for the given purpose. Moreover,
highlighting the different approaches to community detection also delineates
the many lines of research and points out open directions and avenues for
future research.Comment: 14 pages, 2 figures. Written as a chapter for forthcoming Advances in
network clustering and blockmodeling, and based on an extended version of The
many facets of community detection in complex networks, Appl. Netw. Sci. 2: 4
(2017) by the same author
Model-based clustering for populations of networks
Until recently obtaining data on populations of networks was typically rare.
However, with the advancement of automatic monitoring devices and the growing
social and scientific interest in networks, such data has become more widely
available. From sociological experiments involving cognitive social structures
to fMRI scans revealing large-scale brain networks of groups of patients, there
is a growing awareness that we urgently need tools to analyse populations of
networks and particularly to model the variation between networks due to
covariates. We propose a model-based clustering method based on mixtures of
generalized linear (mixed) models that can be employed to describe the joint
distribution of a populations of networks in a parsimonious manner and to
identify subpopulations of networks that share certain topological properties
of interest (degree distribution, community structure, effect of covariates on
the presence of an edge, etc.). Maximum likelihood estimation for the proposed
model can be efficiently carried out with an implementation of the EM
algorithm. We assess the performance of this method on simulated data and
conclude with an example application on advice networks in a small business.Comment: The final (published) version of the article can be downloaded for
free (Open Access) from the editor's website (click on the DOI link below
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