Many political discussions share a common understanding of a rapidly changing world. A global market economy is seen to pose new demands for national markets and is expected to increase the international competition concerning locational factors. Globalization is the keyword of this debate. It describes an economic process that results from changes in the investment, production and distribution decisions made by individual firms and households. Unfortunately, the term globalization can mean different things to different people. Some observers define globalization as market integration (Frieden/ Rogowski 1997); others refer to the internationalization of production processes (Pauly/ Reich 1997; Archibugi / Michie 1998). Though the two concepts arc not mutually exclusive, they neither share a common definition nor do they examine the same processes. Whereas from the macroeconomic perspective ofJeff Frieden and Ronald Rogowski (1997), evidence for a globalizing or even a globalizcd world economy is to be found in the convergence of prices for goods, capital and services, the approach of Daniele Archibugi and Jonathan Michie (1998) regards globalization as a consequence of the growing number of transnational companies. In this paper we concentrate exclusively on the latter perspective and analyze globalization on a sectoral and systemic level. The information that allows us to trace globalization involves sector-specific trade flows, which are available for a period of fourteen years. Network visualizations, a technique that we illustrate more closely in the following section, also help to describe the structural change to the world trade in automobiles on the systemic level.

More and more communities use internet based services and infrastructure for communication and collaboration. All these activities leave digital traces that are of interest for research as real world data sources that can be processed automatically or semi-automatically. Since productive online communities (such as open source developer teams) tend to support the establishment of ties between actors who work on or communicate about the same or similar objects, social network analysis is a frequently used research methodology in this field. A typical application of SNA techniques is the detection of cohesive subgroups of actors (also called "community detection"). A relatively new method for detecting cohesive subgroups is the Clique Percolation Method (CPM), which allows for detecting overlapping subgroups. We have used CPM to analyze data from some open source developer communities (mailing lists and log files) and have compared the results for varied time windows of measurement. The influence of the time span of data capturing/aggregation can be compared to photography: A certain minimal window size is needed to get a clear image with enough "light" (i.e. dense enough interaction data), whereas for very long time spans the image will be blurred because subgroup membership will indeed change during the time span (corresponding to a moving target). In this sense, our target parameter is "resolution" of subgroup structures. We have identified several indicators for good resolution. Applying these indicators to the different CPM results shows the best resolution is a time span of around 2-3 months. In general, this value will vary for different types of communities with different communication frequency and behavior. Following our findings, an explicit analysis and comparison of the influence of time window for different communities may be used to better adjust analysis techniques for the communities at hand.

En este artículo se ofrece un análisis de redes a varios niveles a través del ejemplo de la transferencia de plantas cultivadas dentro del contexto de las redes de correspondencia de Albrecht von Haller y la Sociedad Económica. Por medio de un procedimiento a varios niveles se analizan, una a una, la dinámica cronológica, la estructura social, la distribución espacial y la creación funcional de redes. Estos cuatro niveles de análisis de redes no están en conflicto unos con otros, sino que se complementan mutuamente. El objetivo consiste en comprender mejor cómo contribuyeron dichas redes a la transferencia internacional de conocimientos en el siglo XVIII.

For over 100 years, the German company network was a major feature of organized corporate governance in Germany. This paper uses network visualization techniques and qualitative-historical analysis to discuss the structure, origins and development of this network and to analyze the reasons for its recent erosion. Network visualization makes it possible to identify crucial entanglement patterns that can be traced back historically. In three phases of network formation – the 1880s, 1920s and the 1950s –, capital entanglement resulted from the interplay of company behavior and government policy. In its heyday, the company network was de facto encompassing and provided its core participants, especially the banks, with a national, macroeconomic perspective. In the 1970s, a process of increased competition among financial companies set in. In the 1980s and 1990s, declining returns from blockholding and increased opportunity costs made network dissolution a thinkable option for companies. Because of the strategic reorientation of the largest banks toward investment banking, ties between banks and industry underwent functional changes. Since the year 2000, the German government’s tax policy has sped up network erosion. Vanishing capital ties imply a declining degree of strategic coordination among large German companies.

Obwohl Netzwerkanalysen nunmehr eine fast hundertjährige Geschichte aufweisen, fristet das Paradigma in der deutschen Sozialwissenschaft eher ein Schattendasein. Im Folgenden versuche ich zu verstehen, warum dies so ist, und versuche aufzuzeigen, warum dem Paradigma dennoch eine große Zukunft bevorsteht.