12,907 research outputs found
Network centrality: an introduction
Centrality is a key property of complex networks that influences the behavior
of dynamical processes, like synchronization and epidemic spreading, and can
bring important information about the organization of complex systems, like our
brain and society. There are many metrics to quantify the node centrality in
networks. Here, we review the main centrality measures and discuss their main
features and limitations. The influence of network centrality on epidemic
spreading and synchronization is also pointed out in this chapter. Moreover, we
present the application of centrality measures to understand the function of
complex systems, including biological and cortical networks. Finally, we
discuss some perspectives and challenges to generalize centrality measures for
multilayer and temporal networks.Comment: Book Chapter in "From nonlinear dynamics to complex systems: A
Mathematical modeling approach" by Springe
Dynamic communities in multichannel data: An application to the foreign exchange market during the 2007--2008 credit crisis
We study the cluster dynamics of multichannel (multivariate) time series by
representing their correlations as time-dependent networks and investigating
the evolution of network communities. We employ a node-centric approach that
allows us to track the effects of the community evolution on the functional
roles of individual nodes without having to track entire communities. As an
example, we consider a foreign exchange market network in which each node
represents an exchange rate and each edge represents a time-dependent
correlation between the rates. We study the period 2005-2008, which includes
the recent credit and liquidity crisis. Using dynamical community detection, we
find that exchange rates that are strongly attached to their community are
persistently grouped with the same set of rates, whereas exchange rates that
are important for the transfer of information tend to be positioned on the
edges of communities. Our analysis successfully uncovers major trading changes
that occurred in the market during the credit crisis.Comment: 8 pages, 6 figures, accepted for publication in Chao
Graph Theory and Networks in Biology
In this paper, we present a survey of the use of graph theoretical techniques
in Biology. In particular, we discuss recent work on identifying and modelling
the structure of bio-molecular networks, as well as the application of
centrality measures to interaction networks and research on the hierarchical
structure of such networks and network motifs. Work on the link between
structural network properties and dynamics is also described, with emphasis on
synchronization and disease propagation.Comment: 52 pages, 5 figures, Survey Pape
Self-Healing Protocols for Connectivity Maintenance in Unstructured Overlays
In this paper, we discuss on the use of self-organizing protocols to improve
the reliability of dynamic Peer-to-Peer (P2P) overlay networks. Two similar
approaches are studied, which are based on local knowledge of the nodes' 2nd
neighborhood. The first scheme is a simple protocol requiring interactions
among nodes and their direct neighbors. The second scheme adds a check on the
Edge Clustering Coefficient (ECC), a local measure that allows determining
edges connecting different clusters in the network. The performed simulation
assessment evaluates these protocols over uniform networks, clustered networks
and scale-free networks. Different failure modes are considered. Results
demonstrate the effectiveness of the proposal.Comment: The paper has been accepted to the journal Peer-to-Peer Networking
and Applications. The final publication is available at Springer via
http://dx.doi.org/10.1007/s12083-015-0384-
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