To ensure that services are fulfilled by infrastructures it is extremely important to understand how reliable networks really are and how robustly networks will respond to different types of perturbations.
Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the Internet, the WWW, power grids, transportations systems, food webs, ecosystems, genetic networks, etc. In fact we can say that these networks permeate every aspect of our lives and that our overall well-being depends to a large extent on the stability and strength of these infrastructures. To ensure that services are fulfilled by these infrastructures it is extremely important to understand how reliable these networks really are and how robustly these networks will respond to different types of perturbations.
Within the project “Physics of Complex Networks” we firstly focused on analyzing metrics that can characterize and define the robustness of networks and secondly on treats in networks, such as the spread of viruses.
In order to develop infrastructures, one needs to know what kind of parameters are important to take into account in the design process. The first part of our work has listed several important parameters, studied their use, and analyzed the correlation among the parameters.
The second part of our work relates to epidemic theory, which has a wide range of applications in computer networks, from spreading of malware to information dissemination algorithms. Our society depends more strongly than ever on such computer networks. Many of these networks rely to a large extent on decentralization and self-organization. While decentralization removes obvious vulnerabilities related to single points of failure, it leads to a higher complexity of the system. A more complex type of vulnerability appears in such systems. For instance, computer viruses are imminent threats to all computer networks. We have studied the interaction between malware spreading and strategies that are designed to cope with them.
By studying the robustness of network structures as well as threats or malicious processes within them, we have made important advances in understanding the physics of robust complex networks.