social network analysis has been an established eld since the 1950s; in computer and information sciences, in biology, and of course in mathematics (graph theory) networks are central representations of objects and methods (De Nooy, forthc- ing). More detailed bibliometric studies have examined the individual, cognitive, and institutional composition of complex network theory (Morris and Yen 2004), and social network theory (Otte and Rousseau 2002). Among the more impor- .. tant pieces of literature are Borner et al. (2007), Bornholdt and Schuster (2003), Buchanan (2002), Dorogovtsev and Mendes (2003), Otte and Rousseau (2002), Newman (2003), and Watts (1999, 2004). Of these, Borner .. et al. (2007) stand out because they have most recently re-examined network science, considering it as a possible innovation in information science. All the reviews mentioned include efforts to build bridges between different scienti c disciplines and specialties. In this book we draw particular attention to the link between evolutionary economics and statistical physics.
Despite this impressive development, claims that an entirely new science has been created (Barabasi ' 2002) have nevertheless been the subject of criticism. - depth analyses of a subset of "complex networks" contributions (1991-2003) have shown that the notion of "complex networks" was already prevalent in a number of different elds before it became practically a "brand name" or the popular label for a new specialty area in physics, or a new cross-disciplinary paradigm.
Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Number of pages: 330
Weight: 522 g
Dimensions: 235 x 155 x 18 mm
Edition: Softcover reprint of hardcover 1st ed. 2009
From the reviews:
"This book is a worthwhile contribution aimed at narrowing down the divergence between the socioeconomic research and the physicists' research on networks. ... provides a valuable resource for those interested in how network structures affect innovation and their outcome ... . the book presents a rich set of models and empirical evidence of networks that mainly represent the share of knowledge between nodes (firms). Readers looking for methods and modelling techniques across innovation studies and statistical physics will find this book of valuable use." (Tommaso Ciarli, Journal of Artificial Societies and Social Simulation, Vol. 13 (1), 2010)
"This book is an edited collection of ten articles that address aspects of the relationship between innovation and networks. ... will be of much value not only to those interested in complex economic or social behaviour, but also to those interested in graph-theoretic, statistical, probabilistic, and algebraic structure of networks." (Charles J. Colbourn, Zentralblatt MATH, Vol. 1174, 2009)