5,274 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
Flow Motifs Reveal Limitations of the Static Framework to Represent Human interactions
Networks are commonly used to define underlying interaction structures where
infections, information, or other quantities may spread. Although the standard
approach has been to aggregate all links into a static structure, some studies
suggest that the time order in which the links are established may alter the
dynamics of spreading. In this paper, we study the impact of the time ordering
in the limits of flow on various empirical temporal networks. By using a random
walk dynamics, we estimate the flow on links and convert the original
undirected network (temporal and static) into a directed flow network. We then
introduce the concept of flow motifs and quantify the divergence in the
representativity of motifs when using the temporal and static frameworks. We
find that the regularity of contacts and persistence of vertices (common in
email communication and face-to-face interactions) result on little differences
in the limits of flow for both frameworks. On the other hand, in the case of
communication within a dating site (and of a sexual network), the flow between
vertices changes significantly in the temporal framework such that the static
approximation poorly represents the structure of contacts. We have also
observed that cliques with 3 and 4 vertices con- taining only low-flow links
are more represented than the same cliques with all high-flow links. The
representativity of these low-flow cliques is higher in the temporal framework.
Our results suggest that the flow between vertices connected in cliques depend
on the topological context in which they are placed and in the time sequence in
which the links are established. The structure of the clique alone does not
completely characterize the potential of flow between the vertices
Identifying modular flows on multilayer networks reveals highly overlapping organization in social systems
Unveiling the community structure of networks is a powerful methodology to
comprehend interconnected systems across the social and natural sciences. To
identify different types of functional modules in interaction data aggregated
in a single network layer, researchers have developed many powerful methods.
For example, flow-based methods have proven useful for identifying modular
dynamics in weighted and directed networks that capture constraints on flow in
the systems they represent. However, many networked systems consist of agents
or components that exhibit multiple layers of interactions. Inevitably,
representing this intricate network of networks as a single aggregated network
leads to information loss and may obscure the actual organization. Here we
propose a method based on compression of network flows that can identify
modular flows in non-aggregated multilayer networks. Our numerical experiments
on synthetic networks show that the method can accurately identify modules that
cannot be identified in aggregated networks or by analyzing the layers
separately. We capitalize on our findings and reveal the community structure of
two multilayer collaboration networks: scientists affiliated to the Pierre
Auger Observatory and scientists publishing works on networks on the arXiv.
Compared to conventional aggregated methods, the multilayer method reveals
smaller modules with more overlap that better capture the actual organization
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