13,191 research outputs found
Robustness of the avalanche dynamics in data packet transport on scale-free networks
We study the avalanche dynamics in the data packet transport on scale-free
networks through a simple model. In the model, each vertex is assigned a
capacity proportional to the load with a proportionality constant . When
the system is perturbed by a single vertex removal, the load of each vertex is
redistributed, followed by subsequent failures of overloaded vertices. The
avalanche size depends on the parameter as well as which vertex triggers
it. We find that there exists a critical value at which the avalanche
size distribution follows a power law. The critical exponent associated with it
appears to be robust as long as the degree exponent is between 2 and 3, and is
close in value to that of the distribution of the diameter changes by single
vertex removal.Comment: 5 pages, 7 figures, final version published in PR
Internet data packet transport: from global topology to local queueing dynamics
We study structural feature and evolution of the Internet at the autonomous
systems level. Extracting relevant parameters for the growth dynamics of the
Internet topology, we construct a toy model for the Internet evolution, which
includes the ingredients of multiplicative stochastic evolution of nodes and
edges and adaptive rewiring of edges. The model reproduces successfully
structural features of the Internet at a fundamental level. We also introduce a
quantity called the load as the capacity of node needed for handling the
communication traffic and study its time-dependent behavior at the hubs across
years. The load at hub increases with network size as .
Finally, we study data packet traffic in the microscopic scale. The average
delay time of data packets in a queueing system is calculated, in particular,
when the number of arrival channels is scale-free. We show that when the number
of arriving data packets follows a power law distribution, ,
the queue length distribution decays as and the average delay
time at the hub diverges as in the limit when , being the network degree
exponent.Comment: 5 pages, 4 figures, submitted to International Journal of Bifurcation
and Chao
Evolution of the Protein Interaction Network of Budding Yeast: Role of the Protein Family Compatibility Constraint
Understanding of how protein interaction networks (PIN) of living organisms
have evolved or are organized can be the first stepping stone in unveiling how
life works on a fundamental ground. Here we introduce a hybrid network model
composed of the yeast PIN and the protein family interaction network. The
essential ingredient of the model includes the protein family identity and its
robustness under evolution, as well as the three previously proposed ones: gene
duplication, divergence, and mutation. We investigate diverse structural
properties of our model with parameter values relevant to yeast, finding that
the model successfully reproduces the empirical data.Comment: 5 pages, 5 figures, 1 table. Title changed. Final version published
in JKP
Betweenness centrality correlation in social networks
Scale-free (SF) networks exhibiting a power-law degree distribution can be
grouped into the assortative, dissortative and neutral networks according to
the behavior of the degree-degree correlation coefficient. Here we investigate
the betweenness centrality (BC) correlation for each type of SF networks. While
the BC-BC correlation coefficients behave similarly to the degree-degree
correlation coefficients for the dissortative and neutral networks, the BC
correlation is nontrivial for the assortative ones found mainly in social
networks. The mean BC of neighbors of a vertex with BC is almost
independent of , implying that each person is surrounded by almost the
same influential environments of people no matter how influential the person
is.Comment: 4 pages, 4 figures, 1 tabl
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