17,143 research outputs found
Community detection in multiplex networks using locally adaptive random walks
Multiplex networks, a special type of multilayer networks, are increasingly
applied in many domains ranging from social media analytics to biology. A
common task in these applications concerns the detection of community
structures. Many existing algorithms for community detection in multiplexes
attempt to detect communities which are shared by all layers. In this article
we propose a community detection algorithm, LART (Locally Adaptive Random
Transitions), for the detection of communities that are shared by either some
or all the layers in the multiplex. The algorithm is based on a random walk on
the multiplex, and the transition probabilities defining the random walk are
allowed to depend on the local topological similarity between layers at any
given node so as to facilitate the exploration of communities across layers.
Based on this random walk, a node dissimilarity measure is derived and nodes
are clustered based on this distance in a hierarchical fashion. We present
experimental results using networks simulated under various scenarios to
showcase the performance of LART in comparison to related community detection
algorithms
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
Community Detection in Multiplex Networks
A multiplex network models different modes of interaction among same-type
entities. In this article we provide a taxonomy of community detection
algorithms in multiplex networks. We characterize the different algorithms
based on various properties and we discuss the type of communities detected by
each method. We then provide an extensive experimental evaluation of the
reviewed methods to answer three main questions: to what extent the evaluated
methods are able to detect ground-truth communities, to what extent different
methods produce similar community structures and to what extent the evaluated
methods are scalable. One goal of this survey is to help scholars and
practitioners to choose the right methods for the data and the task at hand,
while also emphasizing when such choice is problematic.Comment: 55 pages. Accepted for publication on ACM Computing Surveys in a
shorter versio
Analysis of Multiplex Social Networks with R
Multiplex social networks are characterized by a common set of actors connected through multiple types of relations. The multinet package provides a set of R functions to analyze multiplex social networks within the more general framework of multilayer networks, where each type of relation is represented as a layer in the network. The package contains functions to import/export, create and manipulate multilayer networks, implementations of several state-of-the-art multiplex network analysis algorithms, e.g., for centrality measures, layer comparison, community detection and visualization. Internally, the package is mainly written in native C++ and integrated with R using the Rcpp package
Community Detection and Improved Detectability in Multiplex Networks
We investigate the widely encountered problem of detecting communities in
multiplex networks, such as social networks, with an unknown arbitrary
heterogeneous structure. To improve detectability, we propose a generative
model that leverages the multiplicity of a single community in multiple layers,
with no prior assumption on the relation of communities among different layers.
Our model relies on a novel idea of incorporating a large set of generic
localized community label constraints across the layers, in conjunction with
the celebrated Stochastic Block Model (SBM) in each layer. Accordingly, we
build a probabilistic graphical model over the entire multiplex network by
treating the constraints as Bayesian priors. We mathematically prove that these
constraints/priors promote existence of identical communities across layers
without introducing further correlation between individual communities. The
constraints are further tailored to render a sparse graphical model and the
numerically efficient Belief Propagation algorithm is subsequently employed. We
further demonstrate by numerical experiments that in the presence of consistent
communities between different layers, consistent communities are matched, and
the detectability is improved over a single layer. We compare our model with a
"correlated model" which exploits the prior knowledge of community correlation
between layers. Similar detectability improvement is obtained under such a
correlation, even though our model relies on much milder assumptions than the
correlated model. Our model even shows a better detection performance over a
certain correlation and signal to noise ratio (SNR) range. In the absence of
community correlation, the correlation model naturally fails, while ours
maintains its performance
A framework for the construction of generative models for mesoscale structure in multilayer networks
Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as time-dependence, multiple subsystems, or both—that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks
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