5,502 research outputs found
Self-organization of network dynamics into local quantized states
Self-organization and pattern formation in network-organized systems emerges
from the collective activation and interaction of many interconnected units. A
striking feature of these non-equilibrium structures is that they are often
localized and robust: only a small subset of the nodes, or cell assembly, is
activated. Understanding the role of cell assemblies as basic functional units
in neural networks and socio-technical systems emerges as a fundamental
challenge in network theory. A key open question is how these elementary
building blocks emerge, and how they operate, linking structure and function in
complex networks. Here we show that a network analogue of the Swift-Hohenberg
continuum model---a minimal-ingredients model of nodal activation and
interaction within a complex network---is able to produce a complex suite of
localized patterns. Hence, the spontaneous formation of robust operational cell
assemblies in complex networks can be explained as the result of
self-organization, even in the absence of synaptic reinforcements. Our results
show that these self-organized, local structures can provide robust functional
units to understand natural and socio-technical network-organized processes.Comment: 11 pages, 4 figure
From neurons to epidemics: How trophic coherence affects spreading processes
Trophic coherence, a measure of the extent to which the nodes of a directed
network are organised in levels, has recently been shown to be closely related
to many structural and dynamical aspects of complex systems, including graph
eigenspectra, the prevalence or absence of feed-back cycles, and linear
stability. Furthermore, non-trivial trophic structures have been observed in
networks of neurons, species, genes, metabolites, cellular signalling,
concatenated words, P2P users, and world trade. Here we consider two simple yet
apparently quite different dynamical models -- one a
Susceptible-Infected-Susceptible (SIS) epidemic model adapted to include
complex contagion, the other an Amari-Hopfield neural network -- and show that
in both cases the related spreading processes are modulated in similar ways by
the trophic coherence of the underlying networks. To do this, we propose a
network assembly model which can generate structures with tunable trophic
coherence, limiting in either perfectly stratified networks or random graphs.
We find that trophic coherence can exert a qualitative change in spreading
behaviour, determining whether a pulse of activity will percolate through the
entire network or remain confined to a subset of nodes, and whether such
activity will quickly die out or endure indefinitely. These results could be
important for our understanding of phenomena such as epidemics, rumours, shocks
to ecosystems, neuronal avalanches, and many other spreading processes
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
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