In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furni...

In this article, I discuss some recent ideas in complex systems on the topic of networks, contained in or inspired by three recent complex systems books. The general science of networks is the subject of Albert-Lazlo Barabási’s Linked [A.-L. Barabási, Linked: The New Science of Networks, Perseus, New York, 2002] and Duncan Watts’ Six Degrees [D. Watts, Six Degrees: The Science of a Connected Ag...

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex theory for topology, it obvious that topology models can be applied to enhance artificial neural models. In this paper, we provide overview most important works published within past 10 years topic theory-based optimizati...

Some years ago a cellular automata model was proposed to describe the evolution of the immune repertoire of B cells and antibodies based on Jerne’s immune network theory and shape-space formalism. Interactions among different B-cell clones, which may be found in low, intermediate and high concentrations, occur depending on the complementarities of their characteristic proteins and are regulated...

This paper presents a model of local network effects in which agents connected in a social network each value the adoption of a product by a heterogeneous subset of other agents in their neighborhood, and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in...

Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their d...

The development of optimization and mathematical models for closed loop supply chain (CLSC) design has attracted considerable interest over the past decades. However, the uncertainties that are inherent in the network design and the complex interactions among various uncertain parameters are challenging the capabilities of the developed tools. The aim of this paper, therefore, is to propose a n...