SIMPLEX 2012 - 4th Annual Workshop on Simplifying Complex Networks for Practitioners

Network science, sometimes also called "complex networks science", has recently attracted much attention from the scientific community, mainly due to the almost ubiquitous presence of complex networks in real-world systems. Examples of complex networks are found in living organisms, in engineering systems, as well as in social networks. Most of the real-world systems have the required degree of complexity to be called "complex systems". Complexity may have to do with the intricate dynamics of the interacting components, with the non-trivial properties of the underlying network topology, or with the sheer size of the system itself.
Despite the numerous workshops and conferences related to network science, it is still a set of loosely interacting communities. Those communities would benefit from better interactions.
Simplex aims at triggering different computer science communities (e.g. communication networks, distributed systems) to propose research areas and topics that should be tackled from the network science perspective. We also seek contributions from network science that are relevant to solve practical computer science problems. Two types of contributions are foreseen from prospective authors. The first type would consist of use-cases of theoretical tools and methods to solve practical problems. Such contributions should be as usable as possible by practitioners in the related field. The second type of contributions would come from practitioners that have identified a problem that may be solved by tools from network sciences. The point of such contributions is to make the network sciences community aware of the importance of a high-impact problem, and to suggest means by which the problem may be solved by the network sciences community. Both contributions should stimulate interaction between theoreticians and practitioners, and also have high potential impact in either field.
Topics for the workshop include, but are not limited to:
Application of complex network theory to the design of web applications;
Data mining of large scale networks;
Analysis of dynamic and time-varying networks;
Network robustness to failures and attacks;
Machine learning and network science;
Complex network theory applied to forwarding/routing problems;
Application of social network analysis to communication and computing system design;
Mobility and connectivity modelling;
Network science and data&information retrieval;
Complex network theory and security applications;