li
1 INTRODUCTION
With the emergence of a variety of mobile data services
with variable coverage, bandwidth, and handoff strategies,
and the need for mobile terminals to roam among these
networks, handoff in hybrid data networks has attracted
tremendous attention [1]. When a mobile terminal moves
away from a base station, the signal level degrades and
there is a need to switch communications to another base
station. For the Internet, this is especially evident. It can be
said, the dynamic switching is a typical feature of the
Internet [2]. So the research of virus propagation in the
typical dynamic switching complex evolving networks is
significant.
The rest of this paper is organized as follows. In Section II
the generation algorithm of dynamic switching complex
evolving networks is presented and a network is generated
with the algorithm. In Section III we discuss the switching
period and scale of nodes and links, and then analyze the
influence of the switching period and scale to virus
propagation. Conclusions of this paper are discussed in
Section VI.
This work is supported by National Nature Science Foundation under
Grant 60973012.
2 THE GENERATION ALGORITHM OF
DYNAMIC SWITCHING COMPLEX
NETWORK MODEL
The model of dynamic switching complex evolving
network is presented based on the Barabási-Albert (BA)
algorithm[3]. The algorithm starts with a small number
m
0
of disconnected nodes, then as follows:
(i) Networks expand continuously by the addition of
new nodes. That is, every time step a new node is added.
(ii) New nodes attach preferentially to sites that are
already well connected. The new node with m links that
are connected to an old node i with probability
()
j
i
j
j
k
k
k
=
∏
∑
,
where k
i
is the degree of the ith node.
After t time steps, the model leads to a scale-free network
with N=t+m
0
nodes and mt edges.
Here we consider m
0
=6 and m=1 in the case of the
scale-free network. The number of nodes is 10000 and the
average degree <k> is 2. The degree distribution of the
scale-free simulated network is shown in Fig.1. As shown
in Figure 1, the percent of nodes which degree is greater
than <k> is 16.4%, the percent of nodes which degree not
greater than the average degree is 83.6％.
On Virus Spreading and Control in Typical Dynamic Switching Complex
Networks
Tao Li
1
, Zhi-Hong Guan
2
, Yuanmei Wang
1
, Weikai Liu
2,3
, Feng Liu
4
1. College of Electronics and Information, Yangtze University, Jingzhou 434023, China
E-mail: tao_hust@yahoo.com.cn
Wangmandy2008@yahoo.com.cn
2. Department of Control Science and Engineering, Huazhong University of science and technology,
Wuhan 430074, China
E-mail: zhhguan@mail.hust.edu.cn
3. School of Science, Wuhan Institute of Technology, Wuhan 430073, China
E-mail: lwkhust@163.com
4. Faculty of Mechanical and Electronic Information Engineering, China University of Geosciences
Wuhan, 430074, China
E-mail: liufengxp@yahoo.com.cn
Abstract: The state of nodes and links of Internet is dynamic; the dynamic switching is the typical features for
Internet. So the research of virus propagation and control in the typical dynamic switching complex evolving
networks is significant. In this paper, the generation algorithm of dynamic switching complex network is
presented, and the influence of switching period and scale to virus propagation is studied as well;
furthermore, the corresponding control strategy is proposed. Finally, numerical simulations confirmed the
results.
Key Words: Dynamic switching, Complex network, Switching period, Switching scale, Control
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