171,536 research outputs found
Uniform synchronous criticality of diversely random complex networks
We investigate collective synchronous behaviors in random complex networks of
limit-cycle oscillators with the non-identical asymmetric coupling scheme, and
find a uniform coupling criticality of collective synchronization which is
independent of complexity of network topologies. Numerically simulations on
categories of random complex networks have verified this conclusion.Comment: 8 pages, 4 figure
Topological fractal networks introduced by mixed degree distribution
Several fundamental properties of real complex networks, such as the
small-world effect, the scale-free degree distribution, and recently discovered
topological fractal structure, have presented the possibility of a unique
growth mechanism and allow for uncovering universal origins of collective
behaviors. However, highly clustered scale-free network, with power-law degree
distribution, or small-world network models, with exponential degree
distribution, are not self-similarity. We investigate networks growth mechanism
of the branching-deactivated geographical attachment preference that learned
from certain empirical evidence of social behaviors. It yields high clustering
and spectrums of degree distribution ranging from algebraic to exponential,
average shortest path length ranging from linear to logarithmic. We observe
that the present networks fit well with small-world graphs and scale-free
networks in both limit cases (exponential and algebraic degree distribution
respectively), obviously lacking self-similar property under a length-scale
transformation. Interestingly, we find perfect topological fractal structure
emerges by a mixture of both algebraic and exponential degree distributions in
a wide range of parameter values. The results present a reliable connection
among small-world graphs, scale-free networks and topological fractal networks,
and promise a natural way to investigate universal origins of collective
behaviors.Comment: 14 pages, 6 figure
Empirical studies on the network of social groups: the case of Tencent QQ
Participation in social groups are important but the collective behaviors of
human as a group are difficult to analyze due to the difficulties to quantify
ordinary social relation, group membership, and to collect a comprehensive
dataset. Such difficulties can be circumvented by analyzing online social
networks. In this paper, we analyze a comprehensive dataset obtained from
Tencent QQ, an instant messenger with the highest market share in China.
Specifically, we analyze three derivative networks involving groups and their
members -- the hypergraph of groups, the network of groups and the user network
-- to reveal social interactions at microscopic and mesoscopic level. Our
results uncover interesting behaviors on the growth of user groups, the
interactions between groups, and their relationship with member age and gender.
These findings lead to insights which are difficult to obtain in ordinary
social networks.Comment: 18 pages, 9 figure
Multiscale modeling of oscillations and spiral waves in Dictyostelium populations
Unicellular organisms exhibit elaborate collective behaviors in response to
environmental cues. These behaviors are controlled by complex biochemical
networks within individual cells and coordinated through cell-to-cell
communication. Describing these behaviors requires new mathematical models that
can bridge scales -- from biochemical networks within individual cells to
spatially structured cellular populations. Here, we present a family of
multiscale models for the emergence of spiral waves in the social amoeba
Dictyostelium discoideum. Our models exploit new experimental advances that
allow for the direct measurement and manipulation of the small signaling
molecule cAMP used by Dictyostelium cells to coordinate behavior in cellular
populations. Inspired by recent experiments, we model the Dictyostelium
signaling network as an excitable system coupled to various pre-processing
modules. We use this family of models to study spatially unstructured
populations by constructing phase diagrams that relate the properties of
population-level oscillations to parameters in the underlying biochemical
network. We then extend our models to include spatial structure and show how
they naturally give rise to spiral waves. Our models exhibit a wide range of
novel phenomena including a density dependent frequency change, bistability,
and dynamic death due to slow cAMP dynamics. Our modeling approach provides a
powerful tool for bridging scales in modeling of Dictyostelium populations
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