669 research outputs found
Preferential Attachment in Online Networks: Measurement and Explanations
We perform an empirical study of the preferential attachment phenomenon in
temporal networks and show that on the Web, networks follow a nonlinear
preferential attachment model in which the exponent depends on the type of
network considered. The classical preferential attachment model for networks by
Barab\'asi and Albert (1999) assumes a linear relationship between the number
of neighbors of a node in a network and the probability of attachment. Although
this assumption is widely made in Web Science and related fields, the
underlying linearity is rarely measured. To fill this gap, this paper performs
an empirical longitudinal (time-based) study on forty-seven diverse Web network
datasets from seven network categories and including directed, undirected and
bipartite networks. We show that contrary to the usual assumption, preferential
attachment is nonlinear in the networks under consideration. Furthermore, we
observe that the deviation from linearity is dependent on the type of network,
giving sublinear attachment in certain types of networks, and superlinear
attachment in others. Thus, we introduce the preferential attachment exponent
as a novel numerical network measure that can be used to discriminate
different types of networks. We propose explanations for the behavior of that
network measure, based on the mechanisms that underly the growth of the network
in question.Comment: 10 pages, 5 figures, Accepted for the WebSci'13 Conference, Paris,
201
Information Super-Diffusion on Structured Networks
We study diffusion of information packets on several classes of structured
networks. Packets diffuse from a randomly chosen node to a specified
destination in the network. As local transport rules we consider random
diffusion and an improved local search method. Numerical simulations are
performed in the regime of stationary workloads away from the jamming
transition. We find that graph topology determines the properties of diffusion
in a universal way, which is reflected by power-laws in the transit-time and
velocity distributions of packets. With the use of multifractal scaling
analysis and arguments of non-extensive statistics we find that these
power-laws are compatible with super-diffusive traffic for random diffusion and
for improved local search. We are able to quantify the role of network topology
on overall transport efficiency. Further, we demonstrate the implications of
improved transport rules and discuss the importance of matching (global)
topology with (local) transport rules for the optimal function of networks. The
presented model should be applicable to a wide range of phenomena ranging from
Internet traffic to protein transport along the cytoskeleton in biological
cells.Comment: 27 pages 7 figure
A critical look at power law modelling of the Internet
This paper takes a critical look at the usefulness of power law models of the
Internet. The twin focuses of the paper are Internet traffic and topology
generation. The aim of the paper is twofold. Firstly it summarises the state of
the art in power law modelling particularly giving attention to existing open
research questions. Secondly it provides insight into the failings of such
models and where progress needs to be made for power law research to feed
through to actual improvements in network performance.Comment: To appear Computer Communication
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