8,301 research outputs found
Characterizing Strategic Cascades on Networks
Transmission of disease, spread of information and rumors, adoption of new
products, and many other network phenomena can be fruitfully modeled as
cascading processes, where actions chosen by nodes influence the subsequent
behavior of neighbors in the network graph. Current literature on cascades
tends to assume nodes choose myopically based on the state of choices already
taken by other nodes. We examine the possibility of strategic choice, where
agents representing nodes anticipate the choices of others who have not yet
decided, and take into account their own influence on such choices. Our study
employs the framework of Chierichetti et al. [2012], who (under assumption of
myopic node behavior) investigate the scheduling of node decisions to promote
cascades of product adoptions preferred by the scheduler. We show that when
nodes behave strategically, outcomes can be extremely different. We exhibit
cases where in the strategic setting 100% of agents adopt, but in the myopic
setting only an arbitrarily small epsilon % do. Conversely, we present cases
where in the strategic setting 0% of agents adopt, but in the myopic setting
(100-epsilon)% do, for any constant epsilon > 0. Additionally, we prove some
properties of cascade processes with strategic agents, both in general and for
particular classes of graphs.Comment: To appear in EC 201
Diffusion in Networks and the Unexpected Virtue of Burstiness
Whether an idea, information, infection, or innovation diffuses throughout a
society depends not only on the structure of the network of interactions, but
also on the timing of those interactions. Recent studies have shown that
diffusion can fail on a network in which people are only active in "bursts",
active for a while and then silent for a while, but diffusion could succeed on
the same network if people were active in a more random Poisson manner. Those
studies generally consider models in which nodes are active according to the
same random timing process and then ask which timing is optimal. In reality,
people differ widely in their activity patterns -- some are bursty and others
are not. Here we show that, if people differ in their activity patterns, bursty
behavior does not always hurt the diffusion, and in fact having some (but not
all) of the population be bursty significantly helps diffusion. We prove that
maximizing diffusion requires heterogeneous activity patterns across agents,
and the overall maximizing pattern of agents' activity times does not involve
any Poisson behavior
Co-evolution of strategy and structure in complex networks with dynamical linking
Here we introduce a model in which individuals differ in the rate at which
they seek new interactions with others, making rational decisions modeled as
general symmetric two-player games. Once a link between two individuals has
formed, the productivity of this link is evaluated. Links can be broken off at
different rates. We provide analytic results for the limiting cases where
linking dynamics is much faster than evolutionary dynamics and vice-versa, and
show how the individual capacity of forming new links or severing inconvenient
ones maps into the problem of strategy evolution in a well-mixed population
under a different game. For intermediate ranges, we investigate numerically the
detailed interplay determined by these two time-scales and show that the scope
of validity of the analytical results extends to a much wider ratio of time
scales than expected
Unravelling the size distribution of social groups with information theory on complex networks
The minimization of Fisher's information (MFI) approach of Frieden et al.
[Phys. Rev. E {\bf 60} 48 (1999)] is applied to the study of size distributions
in social groups on the basis of a recently established analogy between scale
invariant systems and classical gases [arXiv:0908.0504]. Going beyond the ideal
gas scenario is seen to be tantamount to simulating the interactions taking
place in a network's competitive cluster growth process. We find a scaling rule
that allows to classify the final cluster-size distributions using only one
parameter that we call the competitiveness. Empirical city-size distributions
and electoral results can be thus reproduced and classified according to this
competitiveness, which also allows to correctly predict well-established
assessments such as the "six-degrees of separation", which is shown here to be
a direct consequence of the maximum number of stable social relationships that
one person can maintain, known as Dunbar's number. Finally, we show that scaled
city-size distributions of large countries follow the same universal
distribution
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
Topology and energy transport in networks of interacting photosynthetic complexes
We address the role of topology in the energy transport process that occurs
in networks of photosynthetic complexes. We take inspiration from light
harvesting networks present in purple bacteria and simulate an incoherent
dissipative energy transport process on more general and abstract networks,
considering both regular structures (Cayley trees and hyperbranched fractals)
and randomly-generated ones. We focus on the the two primary light harvesting
complexes of purple bacteria, i.e., the LH1 and LH2, and we use
network-theoretical centrality measures in order to select different LH1
arrangements. We show that different choices cause significant differences in
the transport efficiencies, and that for regular networks centrality measures
allow to identify arrangements that ensure transport efficiencies which are
better than those obtained with a random disposition of the complexes. The
optimal arrangements strongly depend on the dissipative nature of the dynamics
and on the topological properties of the networks considered, and depending on
the latter they are achieved by using global vs. local centrality measures. For
randomly-generated networks a random arrangement of the complexes already
provides efficient transport, and this suggests the process is strong with
respect to limited amount of control in the structure design and to the
disorder inherent in the construction of randomly-assembled structures.
Finally, we compare the networks considered with the real biological networks
and find that the latter have in general better performances, due to their
higher connectivity, but the former with optimal arrangements can mimic the
real networks' behaviour for a specific range of transport parameters. These
results show that the use of network-theoretical concepts can be crucial for
the characterization and design of efficient artificial energy transport
networks.Comment: 14 pages, 16 figures, revised versio
Networks as Renormalized Models for Emergent Behavior in Physical Systems
Networks are paradigms for describing complex biological, social and
technological systems. Here I argue that networks provide a coherent framework
to construct coarse-grained models for many different physical systems. To
elucidate these ideas, I discuss two long-standing problems. The first concerns
the structure and dynamics of magnetic fields in the solar corona, as
exemplified by sunspots that startled Galileo almost 400 years ago. We
discovered that the magnetic structure of the corona embodies a scale free
network, with spots at all scales. A network model representing the
three-dimensional geometry of magnetic fields, where links rewire and nodes
merge when they collide in space, gives quantitative agreement with available
data, and suggests new measurements. Seismicity is addressed in terms of
relations between events without imposing space-time windows. A metric
estimates the correlation between any two earthquakes. Linking strongly
correlated pairs, and ignoring pairs with weak correlation organizes the
spatio-temporal process into a sparse, directed, weighted network. New scaling
laws for seismicity are found. For instance, the aftershock decay rate
decreases as 1/t in time up to a correlation time, t[omori]. An estimate from
the data gives t[omori] to be about one year for small magnitude 3 earthquakes,
about 1400 years for the Landers event, and roughly 26,000 years for the
earthquake causing the 2004 Asian tsunami. Our results confirm Kagan's
conjecture that aftershocks can rumble on for centuries.Comment: For the Proceedings of the Erice workshop on Complexity,
Metastability and Nonextensivity (2004), 12 page
Random Sierpinski network with scale-free small-world and modular structure
In this paper, we define a stochastic Sierpinski gasket, on the basis of
which we construct a network called random Sierpinski network (RSN). We
investigate analytically or numerically the statistical characteristics of RSN.
The obtained results reveal that the properties of RSN is particularly rich, it
is simultaneously scale-free, small-world, uncorrelated, modular, and maximal
planar. All obtained analytical predictions are successfully contrasted with
extensive numerical simulations. Our network representation method could be
applied to study the complexity of some real systems in biological and
information fields.Comment: 7 pages, 9 figures; final version accepted for publication in EPJ
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