9,233 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
Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels
Modern network datasets are often composed of multiple layers, either as
different views, time-varying observations, or independent sample units,
resulting in collections of networks over the same set of vertices but with
potentially different connectivity patterns on each network. These data require
models and methods that are flexible enough to capture local and global
differences across the networks, while at the same time being parsimonious and
tractable to yield computationally efficient and theoretically sound solutions
that are capable of aggregating information across the networks. This paper
considers the multilayer degree-corrected stochastic blockmodel, where a
collection of networks share the same community structure, but
degree-corrections and block connection probability matrices are permitted to
be different. We establish the identifiability of this model and propose a
spectral clustering algorithm for community detection in this setting. Our
theoretical results demonstrate that the misclustering error rate of the
algorithm improves exponentially with multiple network realizations, even in
the presence of significant layer heterogeneity with respect to degree
corrections, signal strength, and spectral properties of the block connection
probability matrices. Simulation studies show that this approach improves on
existing multilayer community detection methods in this challenging regime.
Furthermore, in a case study of US airport data through January 2016 --
September 2021, we find that this methodology identifies meaningful community
structure and trends in airport popularity influenced by pandemic impacts on
travel
The use of multilayer network analysis in animal behaviour
Network analysis has driven key developments in research on animal behaviour
by providing quantitative methods to study the social structures of animal
groups and populations. A recent formalism, known as \emph{multilayer network
analysis}, has advanced the study of multifaceted networked systems in many
disciplines. It offers novel ways to study and quantify animal behaviour as
connected 'layers' of interactions. In this article, we review common questions
in animal behaviour that can be studied using a multilayer approach, and we
link these questions to specific analyses. We outline the types of behavioural
data and questions that may be suitable to study using multilayer network
analysis. We detail several multilayer methods, which can provide new insights
into questions about animal sociality at individual, group, population, and
evolutionary levels of organisation. We give examples for how to implement
multilayer methods to demonstrate how taking a multilayer approach can alter
inferences about social structure and the positions of individuals within such
a structure. Finally, we discuss caveats to undertaking multilayer network
analysis in the study of animal social networks, and we call attention to
methodological challenges for the application of these approaches. Our aim is
to instigate the study of new questions about animal sociality using the new
toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl
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