Using graph concepts to understand the organization of complex systems

Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems.Comment: v(1) 38 pages, 7 figures, to appear in the International Journal of Bifurcation and Chaos v(2) Line spacing changed; now 23 pages, 7 figures, to appear in the Special Issue "Complex Networks' Structure and Dynamics'' of the International Journal of Bifurcation and Chaos (Volume 17, Issue 7, July 2007) edited by S. Boccaletti and V. Lator

Encapsulating and representing the knowledge on the evaluation of an engineering system

This paper proposes a cross-disciplinary methodology for a fundamental question in product development: How can the innovation patterns during the evolution of an engineering system (ES) be encapsulated, so that it can later be mined through data mining analysis methods? Reverse engineering answers the question of which components a developed engineering system consists of, and how the components interact to make the working product. TRIZ answers the question of which problem-solving principles can be, or have been employed in developing that system, in comparison to its earlier versions, or with respect to similar systems. While these two methodologies have been very popular, to the best of our knowledge, there does not yet exist a methodology that reverseengineers and encapsulates and represents the information regarding the complete product development process in abstract terms. This paper suggests such a methodology, that consists of mathematical formalism, graph visualization, and database representation. The proposed approach is demonstrated by analyzing the design and development process for a prototype wrist-rehabilitation robot

Dynamical and Structural Modularity of Discrete Regulatory Networks

A biological regulatory network can be modeled as a discrete function that contains all available information on network component interactions. From this function we can derive a graph representation of the network structure as well as of the dynamics of the system. In this paper we introduce a method to identify modules of the network that allow us to construct the behavior of the given function from the dynamics of the modules. Here, it proves useful to distinguish between dynamical and structural modules, and to define network modules combining aspects of both. As a key concept we establish the notion of symbolic steady state, which basically represents a set of states where the behavior of the given function is in some sense predictable, and which gives rise to suitable network modules. We apply the method to a regulatory network involved in T helper cell differentiation

Does technology affect network structure? - A quantitative analysis of collaborative research projects in two specific EU programmes

The promotion of collaborative R&D through Framework Programmes is a top priority of European RTD policy. However, despite the considerable sums involved, surprisingly little is known about the structure of the resulting research networks. Arguing that the underlying technological regime critically affects the structure of collaborative R&D, this article examines the structure and topology of collaborative research networks in the telecommunications and the agro-industrial industry in two specific programmes of the 4th EU Framework Programme. We find systematic differences which we attribute to differences in the underlying knowledge base, the research trajectories pursued in EU-funded R&D and the organisation of knowledge production in the two industries. As expected on the basis of prior research, we show that collaborative research projects involve a larger number of partners and require greater funding in the telecommunications industry, and that actors from science are positioned more prominently in the agro-industrial collaborative R&D network. Contrary to expectations, we find fewer and less intense interactions between science and industry in the agro-industrial industry. We provide a tentative explanation for this result and discuss policy implications.framework programmes, research collaborations, technological regime, sectoral innovation system, social network analysis, science-industry interactions

R&D collaboration networks in the European FrameworkProgrammes: Data processing, network construction and selected results

We describe the construction of a large and novel data set on R&D collaboration networks in the first five EU Framework Programmes (FPs), examine key features and provide economic interpretations for our findings. The data set is based on publicly available raw data that pre-sents numerous challenges. We critically examine the different problems and detail how we have dealt with them. We describe how we construct networks from the processed data. The resulting networks display properties typical for large complex networks, including scale-free degree distributions and the small-world property. The former indicates the presence of net-work hubs, which we identify. Theoretical work shows the latter to be beneficial for knowl-edge creation and diffusion. Structural features are remarkably similar across FPs, indicating similar network formation mechanisms despite changes in governance rules. Several findings point towards the existence of a stable core of interlinked actors since the early FPs with inte-gration increasing over time. This core consists mainly of universities and research organisa-tions. The paper concludes with an agenda for future research.R&D collaboration, EU Framework Programmes, complex networks, small world effect, knowledge creation, knowledge diffusion, European Research Area

Biological Organisms as Semiosic Systems: the importance of strong and weak anticipation

The biological realm is examined as a semiosic system that transforms basic matter into a complex and intimately networked diversity of morphological forms according to generic sets of self‐generated rules of formation. Semiosis is understood to operate as a function f(x)=y where the mediative rules of formation, f, operate within predictive or anticipatory capacities. Strong and weak anticipation are examined and the paper concludes that strong anticipation, operating as a virtual or imaginary hypothesis construction is a basic property of the biological realm. Strong anticipation enables the biological species to develop multiple hypothetical ‘network motifs’ about its future activities within the environment. The species will ‘choose’ one of these probabilities – any of which would be functional – to articulate in actual time and space. This theory rejects random mutation as the source of innovative evolution and adaptation. Weak anticipation is defined as Natural Selection and is described as a post hoc model of strong anticipation’s ‘selected solution’