3,523 research outputs found
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks
Multiplex networks have become increasingly more prevalent in many fields,
and have emerged as a powerful tool for modeling the complexity of real
networks. There is a critical need for developing inference models for
multiplex networks that can take into account potential dependencies across
different layers, particularly when the aim is community detection. We add to a
limited literature by proposing a novel and efficient Bayesian model for
community detection in multiplex networks. A key feature of our approach is the
ability to model varying communities at different network layers. In contrast,
many existing models assume the same communities for all layers. Moreover, our
model automatically picks up the necessary number of communities at each layer
(as validated by real data examples). This is appealing, since deciding the
number of communities is a challenging aspect of community detection, and
especially so in the multiplex setting, if one allows the communities to change
across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a
hierarchical Dirichlet prior to model community labels across layers, allowing
dependency in their structure. Given the community labels, a stochastic block
model (SBM) is assumed for each layer. We develop an efficient slice sampler
for sampling the posterior distribution of the community labels as well as the
link probabilities between communities. In doing so, we address some unique
challenges posed by coupling the complex likelihood of SBM with the
hierarchical nature of the prior on the labels. An extensive empirical
validation is performed on simulated and real data, demonstrating the superior
performance of the model over single-layer alternatives, as well as the ability
to uncover interesting structures in real networks
Multiplex Communities and the Emergence of International Conflict
Advances in community detection reveal new insights into multiplex and
multilayer networks. Less work, however, investigates the relationship between
these communities and outcomes in social systems. We leverage these advances to
shed light on the relationship between the cooperative mesostructure of the
international system and the onset of interstate conflict. We detect
communities based upon weaker signals of affinity expressed in United Nations
votes and speeches, as well as stronger signals observed across multiple layers
of bilateral cooperation. Communities of diplomatic affinity display an
expected negative relationship with conflict onset. Ties in communities based
upon observed cooperation, however, display no effect under a standard model
specification and a positive relationship with conflict under an alternative
specification. These results align with some extant hypotheses but also point
to a paucity in our understanding of the relationship between community
structure and behavioral outcomes in networks.Comment: arXiv admin note: text overlap with arXiv:1802.0039
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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