2,655 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
Communities in Networks
We survey some of the concepts, methods, and applications of community
detection, which has become an increasingly important area of network science.
To help ease newcomers into the field, we provide a guide to available
methodology and open problems, and discuss why scientists from diverse
backgrounds are interested in these problems. As a running theme, we emphasize
the connections of community detection to problems in statistical physics and
computational optimization.Comment: survey/review article on community structure in networks; published
version is available at
http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd
Hypergraphx: a library for higher-order network analysis
From social to biological systems, many real-world systems are characterized
by higher-order, non-dyadic interactions. Such systems are conveniently
described by hypergraphs, where hyperedges encode interactions among an
arbitrary number of units. Here, we present an open-source python library,
hypergraphx (HGX), providing a comprehensive collection of algorithms and
functions for the analysis of higher-order networks. These include different
ways to convert data across distinct higher-order representations, a large
variety of measures of higher-order organization at the local and the
mesoscale, statistical filters to sparsify higher-order data, a wide array of
static and dynamic generative models, and an implementation of different
dynamical processes with higher-order interactions. Our computational framework
is general, and allows to analyse hypergraphs with weighted, directed, signed,
temporal and multiplex group interactions. We provide visual insights on
higher-order data through a variety of different visualization tools. We
accompany our code with an extended higher-order data repository, and
demonstrate the ability of HGX to analyse real-world systems through a
systematic analysis of a social network with higher-order interactions. The
library is conceived as an evolving, community-based effort, which will further
extend its functionalities over the years. Our software is available at
https://github.com/HGX-Team/hypergraph
Inferring monopartite projections of bipartite networks: an entropy-based approach
Bipartite networks are currently regarded as providing a major insight into
the organization of many real-world systems, unveiling the mechanisms driving
the interactions occurring between distinct groups of nodes. One of the most
important issues encountered when modeling bipartite networks is devising a way
to obtain a (monopartite) projection on the layer of interest, which preserves
as much as possible the information encoded into the original bipartite
structure. In the present paper we propose an algorithm to obtain
statistically-validated projections of bipartite networks, according to which
any two nodes sharing a statistically-significant number of neighbors are
linked. Since assessing the statistical significance of nodes similarity
requires a proper statistical benchmark, here we consider a set of four null
models, defined within the exponential random graph framework. Our algorithm
outputs a matrix of link-specific p-values, from which a validated projection
is straightforwardly obtainable, upon running a multiple hypothesis testing
procedure. Finally, we test our method on an economic network (i.e. the
countries-products World Trade Web representation) and a social network (i.e.
MovieLens, collecting the users' ratings of a list of movies). In both cases
non-trivial communities are detected: while projecting the World Trade Web on
the countries layer reveals modules of similarly-industrialized nations,
projecting it on the products layer allows communities characterized by an
increasing level of complexity to be detected; in the second case, projecting
MovieLens on the films layer allows clusters of movies whose affinity cannot be
fully accounted for by genre similarity to be individuated.Comment: 16 pages, 9 figure
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