519,578 research outputs found
Complex Networks from Simple Rewrite Systems
Complex networks are all around us, and they can be generated by simple
mechanisms. Understanding what kinds of networks can be produced by following
simple rules is therefore of great importance. We investigate this issue by
studying the dynamics of extremely simple systems where are `writer' moves
around a network, and modifies it in a way that depends upon the writer's
surroundings. Each vertex in the network has three edges incident upon it,
which are colored red, blue and green. This edge coloring is done to provide a
way for the writer to orient its movement. We explore the dynamics of a space
of 3888 of these `colored trinet automata' systems. We find a large variety of
behaviour, ranging from the very simple to the very complex. We also discover
simple rules that generate forms which are remarkably similar to a wide range
of natural objects. We study our systems using simulations (with appropriate
visualization techniques) and analyze selected rules mathematically. We arrive
at an empirical classification scheme which reveals a lot about the kinds of
dynamics and networks that can be generated by these systems
The Regularizing Capacity of Metabolic Networks
Despite their topological complexity almost all functional properties of
metabolic networks can be derived from steady-state dynamics. Indeed, many
theoretical investigations (like flux-balance analysis) rely on extracting
function from steady states. This leads to the interesting question, how
metabolic networks avoid complex dynamics and maintain a steady-state behavior.
Here, we expose metabolic network topologies to binary dynamics generated by
simple local rules. We find that the networks' response is highly specific:
Complex dynamics are systematically reduced on metabolic networks compared to
randomized networks with identical degree sequences. Already small topological
modifications substantially enhance the capacity of a network to host complex
dynamic behavior and thus reduce its regularizing potential. This exceptionally
pronounced regularization of dynamics encoded in the topology may explain, why
steady-state behavior is ubiquitous in metabolism.Comment: 6 pages, 4 figure
Complex networks as an emerging property of hierarchical preferential attachment
Real complex systems are not rigidly structured; no clear rules or blueprints
exist for their construction. Yet, amidst their apparent randomness, complex
structural properties universally emerge. We propose that an important class of
complex systems can be modeled as an organization of many embedded levels
(potentially infinite in number), all of them following the same universal
growth principle known as preferential attachment. We give examples of such
hierarchy in real systems, for instance in the pyramid of production entities
of the film industry. More importantly, we show how real complex networks can
be interpreted as a projection of our model, from which their scale
independence, their clustering, their hierarchy, their fractality and their
navigability naturally emerge. Our results suggest that complex networks,
viewed as growing systems, can be quite simple, and that the apparent
complexity of their structure is largely a reflection of their unobserved
hierarchical nature.Comment: 12 pages, 7 figure
Symbolic dynamics of biological feedback networks
We formulate general rules for a coarse-graining of the dynamics, which we
term `symbolic dynamics', of feedback networks with monotone interactions, such
as most biological modules. Networks which are more complex than simple cyclic
structures can exhibit multiple different symbolic dynamics. Nevertheless, we
show several examples where the symbolic dynamics is dominated by a single
pattern that is very robust to changes in parameters and is consistent with the
dynamics being dictated by a single feedback loop. Our analysis provides a
method for extracting these dominant loops from short time series, even if they
only show transient trajectories.Comment: 4 pages, 4 figure
Self-organization of Nodes using Bio-Inspired Techniques for Achieving Small World Properties
In an autonomous wireless sensor network, self-organization of the nodes is
essential to achieve network wide characteristics. We believe that connectivity
in wireless autonomous networks can be increased and overall average path
length can be reduced by using beamforming and bio-inspired algorithms. Recent
works on the use of beamforming in wireless networks mostly assume the
knowledge of the network in aggregation to either heterogeneous or hybrid
deployment. We propose that without the global knowledge or the introduction of
any special feature, the average path length can be reduced with the help of
inspirations from the nature and simple interactions between neighboring nodes.
Our algorithm also reduces the number of disconnected components within the
network. Our results show that reduction in the average path length and the
number of disconnected components can be achieved using very simple local rules
and without the full network knowledge.Comment: Accepted to Joint workshop on complex networks and pervasive group
communication (CCNet/PerGroup), in conjunction with IEEE Globecom 201
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