The idea of representing a complex interacting systems of human and/or non-human actors as a network is very old. Already Aristotle in his Politicsconsiders the society as a community of different individuals clustered in several subgroups and is aware of the founding dichotomy of networks: the individual nodes that pursue their own interests, as opposed to the health of the net as an impersonal whole. Networks and webs appear in a manifold of areas of human activities. It is therefore no surprise that they have played a role - for decades already - in many research areas in natural sciences, originally as a convenient representation form and more recently as a deeper paradigm, too. Let us mention the invention of Feynman diagrams, developed in 1948 by Nobel Prize-winning physicist Richard Feynman to allow for a compact representation of interactions between sub-atomic particles in quantum field theory, which have ever since mutated into a source of extremely efficient computational algorithms. The medium is (part of) the message, in this and in many further network-based schemes that arise in natural sciences.
Today network methods are being adopted at an ever-faster rate, to provide a convenient description of a model, or perhaps to perform computations making use of a combinatorial approach, or finally to convey information among subsystems in a more efficient way. Now they are crucial theoretical tools in fields as far away as biology (ecological networks, neuroscience), physics (lattice field theories), computer sciences (decision trees), civil engineering (traffic flows on road networks), just to name a few.
Seemingly, the sole common point to all these studies is the use of a network formalism. To introduce these systems, which describe completely different objects/actors but usually have similar qualitative properties, completely different methods are often used - at a first glance. Indeed, many applied scientists make sometimes use (often in a naive, enthusiastic, and rewarding way) of mathematical tools and notions. Far from being only an elegant human construct out of any touch with reality, modern mathematics - combinatorics and analysis in the first place - is a set of key technologies that are particularly mighty in the analysis of network-shaped systems. Not despite but rather exactly because of their abstraction, mathematical methods are applicable to a manifold of different fields. This research project will bring together researchers whose research areas benefit from, or feature approaches based on, or are directly involved with, the analysis of graphs and networks. We are going to concentrate our investigations on evolutionary networks describing systems where not only the individual nodes, but also the interactions between the nodes are subject to time evolution, possibly along with changes of the underlying network topology, with a special focus on applications in physical and natural sciences.
Our main bet will be that mathematics can (and in fact, following Galileo Galilei's opinion on the language of the universe, ought to) be the common language of this loose network of network-conscious scientists. Our aim will be to initiate interdisciplinary collaborations and mutual interactions, even with pure mathematicians who already have experience with accurate description of complicated systems.
Please direct any inquiries about the scientific program of the Cooperation Group to Jun.-Prof. PD Dr. Delio Mugnolo (email@example.com), PD Dr. Fatihcan M. Atay (firstname.lastname@example.org) and Prof. Dr. Pavel Kurasov (email@example.com)