DYNAMICS ON AND OF COMPLEX NETWORKS II

Time of the event
Event's start:: 
18 Sep 2008
Event's end:: 
19 Sep 2008

Aim

Large-scale networks with complex interaction patterns between elements are found in abundance in both nature and man-made systems (e.g., genetic pathways, ecological networks, social networks, networks of scientific collaboration, WWW, peer-to-peer networks, power grid etc.). The main aim of this workshop is to explore the statistical dynamics on and of such networks. Dynamics on networks refers to the different types of so called processes (e.g. proliferation, diffusion etc.) that take place on networks. The functionality/efficiency of such processes is strongly affected by the topology as well as the dynamic behavior of the network. On the other hand, Dynamics of networks mainly refers to various phenomena (for instance self-organization) that go on in order to bring about certain changes in the topology of the network.

After the intense debate held at Dynamics On and Of Complex Networks - I, it became clear that the stability and robustness of highly dynamical networks as in ad-hoc networks of mobile agents, and study of dynamical process on transport networks are the hottest new theoretical challenges for Complex Network research. Accordingly, Dynamics On and Of Complex Networks II will focus on these topics.

Call for Papers
The scope of the workshop includes, but is not restricted to, the following topics:

* Robustness & Stability of dynamical networks
* Mass spreading in transportation networks
* Dynamical processes on transportation networks (e.g. spreading of diseases)
* Search on networks
* Rewiring and evolution of dynamical networks
* Network structure of a system of moving agents

Not restricted to these topics, the workshop will also welcome contribution on:

* Evolution of the network
* Optimization going on in the network
* Decay of the network
* Robustness & Stability of the network
* Attacks on the network
* Merging of networks
* Aesthetics and Visualization of networks
* Spatial growth of the network
* Self-similarity of the network