14,270 research outputs found
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
A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks
This paper presents a novel spectral algorithm with additive clustering
designed to identify overlapping communities in networks. The algorithm is
based on geometric properties of the spectrum of the expected adjacency matrix
in a random graph model that we call stochastic blockmodel with overlap (SBMO).
An adaptive version of the algorithm, that does not require the knowledge of
the number of hidden communities, is proved to be consistent under the SBMO
when the degrees in the graph are (slightly more than) logarithmic. The
algorithm is shown to perform well on simulated data and on real-world graphs
with known overlapping communities.Comment: Journal of Theoretical Computer Science (TCS), Elsevier, A Para\^itr
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