30,012 research outputs found
Adaptive Network Dynamics and Evolution of Leadership in Collective Migration
The evolution of leadership in migratory populations depends not only on
costs and benefits of leadership investments but also on the opportunities for
individuals to rely on cues from others through social interactions. We derive
an analytically tractable adaptive dynamic network model of collective
migration with fast timescale migration dynamics and slow timescale adaptive
dynamics of individual leadership investment and social interaction. For large
populations, our analysis of bifurcations with respect to investment cost
explains the observed hysteretic effect associated with recovery of migration
in fragmented environments. Further, we show a minimum connectivity threshold
above which there is evolutionary branching into leader and follower
populations. For small populations, we show how the topology of the underlying
social interaction network influences the emergence and location of leaders in
the adaptive system. Our model and analysis can describe other adaptive network
dynamics involving collective tracking or collective learning of a noisy,
unknown signal, and likewise can inform the design of robotic networks where
agents use decentralized strategies that balance direct environmental
measurements with agent interactions.Comment: Submitted to Physica D: Nonlinear Phenomen
A novel approach to study realistic navigations on networks
We consider navigation or search schemes on networks which are realistic in
the sense that not all search chains can be completed. We show that the
quantity , where is the average dynamic shortest distance
and the success rate of completion of a search, is a consistent measure
for the quality of a search strategy. Taking the example of realistic searches
on scale-free networks, we find that scales with the system size as
, where decreases as the searching strategy is improved.
This measure is also shown to be sensitive to the distintinguishing
characteristics of networks. In this new approach, a dynamic small world (DSW)
effect is said to exist when . We show that such a DSW indeed
exists in social networks in which the linking probability is dependent on
social distances.Comment: Text revised, references added; accepted version in Journal of
Statistical Mechanic
Networking strategies in streptomyces coelicolor
We are interested the soil dwelling bacteria Streptomyces coelicolor because its cells grow end to end in a line. New branches have the potential to extend from any point along this line and the result is a network of branches and connections. This is a novel form of colonisation in the bacterial world and it is advantageous for spreading through an environment resourcefully. Networking protocols for communication technologies have similar pressures to be resourceful in terms of time, computing power, and energy. In this preliminary investigation we design a computer model of the biological system to understand its limitations and strategies for survival. The decentralised capacity for organisation of both the bacterial system and the model reflects well on the now-popular conventions for path finding and ad hoc network building in human technologies. The project will ultimately become a comparison of strategies between nature and the man-made
Epidemics in partially overlapped multiplex networks
Many real networks exhibit a layered structure in which links in each layer
reflect the function of nodes on different environments. These multiple types
of links are usually represented by a multiplex network in which each layer has
a different topology. In real-world networks, however, not all nodes are
present on every layer. To generate a more realistic scenario, we use a
generalized multiplex network and assume that only a fraction of the nodes
are shared by the layers. We develop a theoretical framework for a branching
process to describe the spread of an epidemic on these partially overlapped
multiplex networks. This allows us to obtain the fraction of infected
individuals as a function of the effective probability that the disease will be
transmitted . We also theoretically determine the dependence of the epidemic
threshold on the fraction of shared nodes in a system composed of two
layers. We find that in the limit of the threshold is dominated by
the layer with the smaller isolated threshold. Although a system of two
completely isolated networks is nearly indistinguishable from a system of two
networks that share just a few nodes, we find that the presence of these few
shared nodes causes the epidemic threshold of the isolated network with the
lower propagating capacity to change discontinuously and to acquire the
threshold of the other network.Comment: 13 pages, 4 figure
Pioneers of Influence Propagation in Social Networks
With the growing importance of corporate viral marketing campaigns on online
social networks, the interest in studies of influence propagation through
networks is higher than ever. In a viral marketing campaign, a firm initially
targets a small set of pioneers and hopes that they would influence a sizeable
fraction of the population by diffusion of influence through the network. In
general, any marketing campaign might fail to go viral in the first try. As
such, it would be useful to have some guide to evaluate the effectiveness of
the campaign and judge whether it is worthy of further resources, and in case
the campaign has potential, how to hit upon a good pioneer who can make the
campaign go viral. In this paper, we present a diffusion model developed by
enriching the generalized random graph (a.k.a. configuration model) to provide
insight into these questions. We offer the intuition behind the results on this
model, rigorously proved in Blaszczyszyn & Gaurav(2013), and illustrate them
here by taking examples of random networks having prototypical degree
distributions - Poisson degree distribution, which is commonly used as a kind
of benchmark, and Power Law degree distribution, which is normally used to
approximate the real-world networks. On these networks, the members are assumed
to have varying attitudes towards propagating the information. We analyze three
cases, in particular - (1) Bernoulli transmissions, when a member influences
each of its friend with probability p; (2) Node percolation, when a member
influences all its friends with probability p and none with probability 1-p;
(3) Coupon-collector transmissions, when a member randomly selects one of his
friends K times with replacement. We assume that the configuration model is the
closest approximation of a large online social network, when the information
available about the network is very limited. The key insight offered by this
study from a firm's perspective is regarding how to evaluate the effectiveness
of a marketing campaign and do cost-benefit analysis by collecting relevant
statistical data from the pioneers it selects. The campaign evaluation
criterion is informed by the observation that if the parameters of the
underlying network and the campaign effectiveness are such that the campaign
can indeed reach a significant fraction of the population, then the set of good
pioneers also forms a significant fraction of the population. Therefore, in
such a case, the firms can even adopt the naive strategy of repeatedly picking
and targeting some number of pioneers at random from the population. With this
strategy, the probability of them picking a good pioneer will increase
geometrically fast with the number of tries
Graphs with specified degree distributions, simple epidemics and local vaccination strategies
Consider a random graph, having a pre-specified degree distribution F but
other than that being uniformly distributed, describing the social structure
(friendship) in a large community. Suppose one individual in the community is
externally infected by an infectious disease and that the disease has its
course by assuming that infected individuals infect their not yet infected
friends independently with probability p. For this situation the paper
determines R_0 and tau_0, the basic reproduction number and the asymptotic
final size in case of a major outbreak. Further, the paper looks at some
different local vaccination strategies where individuals are chosen randomly
and vaccinated, or friends of the selected individuals are vaccinated, prior to
the introduction of the disease. For the studied vaccination strategies the
paper determines R_v: the reproduction number, and tau_v: the asymptotic final
proportion infected in case of a major outbreak, after vaccinating a fraction
v.Comment: 31 pages, 3 figure
Do Diffusion Protocols Govern Cascade Growth?
Large cascades can develop in online social networks as people share
information with one another. Though simple reshare cascades have been studied
extensively, the full range of cascading behaviors on social media is much more
diverse. Here we study how diffusion protocols, or the social exchanges that
enable information transmission, affect cascade growth, analogous to the way
communication protocols define how information is transmitted from one point to
another. Studying 98 of the largest information cascades on Facebook, we find a
wide range of diffusion protocols - from cascading reshares of images, which
use a simple protocol of tapping a single button for propagation, to the ALS
Ice Bucket Challenge, whose diffusion protocol involved individuals creating
and posting a video, and then nominating specific others to do the same. We
find recurring classes of diffusion protocols, and identify two key
counterbalancing factors in the construction of these protocols, with
implications for a cascade's growth: the effort required to participate in the
cascade, and the social cost of staying on the sidelines. Protocols requiring
greater individual effort slow down a cascade's propagation, while those
imposing a greater social cost of not participating increase the cascade's
adoption likelihood. The predictability of transmission also varies with
protocol. But regardless of mechanism, the cascades in our analysis all have a
similar reproduction number ( 1.8), meaning that lower rates of
exposure can be offset with higher per-exposure rates of adoption. Last, we
show how a cascade's structure can not only differentiate these protocols, but
also be modeled through branching processes. Together, these findings provide a
framework for understanding how a wide variety of information cascades can
achieve substantial adoption across a network.Comment: ICWSM 201
Epidemic model with isolation in multilayer networks
The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the isolation of infected individuals is disregarded. Hence we focus our study in an epidemic model in a two-layer network and we use an isolation parameter w to measure the effect of quarantining infected individuals from both layers during an isolation period tw. We call this process the Susceptible-Infected-Isolated-Recovered (SIIR) model. Using the framework of link percolation we find that isolation increases the critical epidemic threshold of the disease because the time in which infection can spread is reduced. In this scenario we find that this threshold increases with w and tw. When the isolation period is maximum there is a critical threshold for w above which the disease never becomes an epidemic. We simulate the process and find an excellent agreement with the theoretical results.We thank the NSF (grants CMMI 1125290 and CHE-1213217) and the Keck Foundation for financial support. LGAZ and LAB wish to thank to UNMdP and FONCyT (Pict 0429/2013) for financial support. (CMMI 1125290 - NSF; CHE-1213217 - NSF; Keck Foundation; UNMdP; Pict 0429/2013 - FONCyT)Published versio
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