11,262 research outputs found
Measure of Node Similarity in Multilayer Networks
The weight of links in a network is often related to the similarity of the
nodes. Here, we introduce a simple tunable measure for analysing the similarity
of nodes across different link weights. In particular, we use the measure to
analyze homophily in a group of 659 freshman students at a large university.
Our analysis is based on data obtained using smartphones equipped with custom
data collection software, complemented by questionnaire-based data. The network
of social contacts is represented as a weighted multilayer network constructed
from different channels of telecommunication as well as data on face-to-face
contacts. We find that even strongly connected individuals are not more similar
with respect to basic personality traits than randomly chosen pairs of
individuals. In contrast, several socio-demographics variables have a
significant degree of similarity. We further observe that similarity might be
present in one layer of the multilayer network and simultaneously be absent in
the other layers. For a variable such as gender, our measure reveals a
transition from similarity between nodes connected with links of relatively low
weight to dis-similarity for the nodes connected by the strongest links. We
finally analyze the overlap between layers in the network for different levels
of acquaintanceships.Comment: 12 pages, 4 figure
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
Constrained information flows in temporal networks reveal intermittent communities
Many real-world networks represent dynamic systems with interactions that
change over time, often in uncoordinated ways and at irregular intervals. For
example, university students connect in intermittent groups that repeatedly
form and dissolve based on multiple factors, including their lectures,
interests, and friends. Such dynamic systems can be represented as multilayer
networks where each layer represents a snapshot of the temporal network. In
this representation, it is crucial that the links between layers accurately
capture real dependencies between those layers. Often, however, these
dependencies are unknown. Therefore, current methods connect layers based on
simplistic assumptions that do not capture node-level layer dependencies. For
example, connecting every node to itself in other layers with the same weight
can wipe out dependencies between intermittent groups, making it difficult or
even impossible to identify them. In this paper, we present a principled
approach to estimating node-level layer dependencies based on the network
structure within each layer. We implement our node-level coupling method in the
community detection framework Infomap and demonstrate its performance compared
to current methods on synthetic and real temporal networks. We show that our
approach more effectively constrains information inside multilayer communities
so that Infomap can better recover planted groups in multilayer benchmark
networks that represent multiple modes with different groups and better
identify intermittent communities in real temporal contact networks. These
results suggest that node-level layer coupling can improve the modeling of
information spreading in temporal networks and better capture intermittent
community structure.Comment: 10 pages, 10 figures, published in PR
struc2vec: Learning Node Representations from Structural Identity
Structural identity is a concept of symmetry in which network nodes are
identified according to the network structure and their relationship to other
nodes. Structural identity has been studied in theory and practice over the
past decades, but only recently has it been addressed with representational
learning techniques. This work presents struc2vec, a novel and flexible
framework for learning latent representations for the structural identity of
nodes. struc2vec uses a hierarchy to measure node similarity at different
scales, and constructs a multilayer graph to encode structural similarities and
generate structural context for nodes. Numerical experiments indicate that
state-of-the-art techniques for learning node representations fail in capturing
stronger notions of structural identity, while struc2vec exhibits much superior
performance in this task, as it overcomes limitations of prior approaches. As a
consequence, numerical experiments indicate that struc2vec improves performance
on classification tasks that depend more on structural identity.Comment: 10 pages, KDD2017, Research Trac
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
- …