5,177 research outputs found
Assortativity and leadership emergence from anti-preferential attachment in heterogeneous networks
Many real-world networks exhibit degree-assortativity, with nodes of similar
degree more likely to link to one another. Particularly in social networks, the
contribution to the total assortativity varies with degree, featuring a
distinctive peak slightly past the average degree. The way traditional models
imprint assortativity on top of pre-defined topologies is via degree-preserving
link permutations, which however destroy the particular graph's hierarchical
traits of clustering. Here, we propose the first generative model which creates
heterogeneous networks with scale-free-like properties and tunable realistic
assortativity. In our approach, two distinct populations of nodes are added to
an initial network seed: one (the followers) that abides by usual preferential
rules, and one (the potential leaders) connecting via anti-preferential
attachments, i.e. selecting lower degree nodes for their initial links. The
latter nodes come to develop a higher average degree, and convert eventually
into the final hubs. Examining the evolution of links in Facebook, we present
empirical validation for the connection between the initial anti-preferential
attachment and long term high degree. Thus, our work sheds new light on the
structure and evolution of social networks
Local-world evolving networks with tunable clustering
We propose an extended local-world evolving network model including a triad
formation step. In the process of network evolution, random fluctuation in the
number of new edges is involved. We derive analytical expressions for degree
distribution, clustering coefficient and average path length. Our model can
unify the generic properties of real-life networks: scale-free degree
distribution, high clustering and small inter-node separation. Moreover, in our
model, the clustering coefficient is tunable simply by changing the expected
number of triad formation steps after a single local preferential attachment
step.Comment: 18 pages, 6 figures. Accepted for publication in Physica
Tuning the average path length of complex networks and its influence to the emergent dynamics of the majority-rule model
We show how appropriate rewiring with the aid of Metropolis Monte Carlo
computational experiments can be exploited to create network topologies
possessing prescribed values of the average path length (APL) while keeping the
same connectivity degree and clustering coefficient distributions. Using the
proposed rewiring rules we illustrate how the emergent dynamics of the
celebrated majority-rule model are shaped by the distinct impact of the APL
attesting the need for developing efficient algorithms for tuning such network
characteristics.Comment: 10 figure
On the formation of structure in growing networks
Based on the formation of triad junctions, the proposed mechanism generates
networks that exhibit extended rather than single power law behavior. Triad
formation guarantees strong neighborhood clustering and community-level
characteristics as the network size grows to infinity. The asymptotic behavior
is of interest in the study of directed networks in which (i) the formation of
links cannot be described according to the principle of preferential
attachment; (ii) the in-degree distribution fits a power law for nodes with a
high degree and an exponential form otherwise; (iii) clustering properties
emerge at multiple scales and depend on both the number of links that newly
added nodes establish and the probability of forming triads; and (iv) groups of
nodes form modules that feature less links to the rest of the nodes.Comment: 17 pages, 9 figures, we apply the proposed mechanism to generate
network realizations that resemble the degree distribution and clustering
properties of an empirical network with no directed cycles (i.e., when the
model parameter n=0), updated reference
The effect of aging on network structure
In network evolution, the effect of aging is universal: in scientific
collaboration network, scientists have a finite time span of being active; in
movie actors network, once popular stars are retiring from stage; devices on
the Internet may become outmoded with techniques developing so rapidly. Here we
find in citation networks that this effect can be represented by an exponential
decay factor, , where is the node age, while other
evolving networks (the Internet for instance) may have different types of
aging, for example, a power-law decay factor, which is also studied and
compared. It has been found that as soon as such a factor is introduced to the
Barabasi-Albert Scale-Free model, the network will be significantly
transformed. The network will be clustered even with infinitely large size, and
the clustering coefficient varies greatly with the intensity of the aging
effect, i.e. it increases linearly with for small values of
and decays exponentially for large values of . At the same time, the
aging effect may also result in a hierarchical structure and a disassortative
degree-degree correlation. Generally the aging effect will increase the average
distance between nodes, but the result depends on the type of the decay factor.
The network appears like a one-dimensional chain when exponential decay is
chosen, but with power-law decay, a transformation process is observed, i.e.,
from a small-world network to a hypercubic lattice, and to a one-dimensional
chain finally. The disparities observed for different choices of the decay
factor, in clustering, average node distance and probably other aspects not yet
identified, are believed to bear significant meaning on empirical data
acquisition.Comment: 8 pages, 9 figures,V2, accepted for publication in Phys. Rev.
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