Order this book
Corvinus University of Budapest, Institute of Sociology and Social Policy
Network science is a meeting place of disciplines and it is not and should not be a privatized territory of physicists, economists or sociologists. This idea determines the basic character of this book. This is the first time that a book gives a comprehensive view on modeling and studying networks by integrating contributions from a wide range of disciplines.
Most prominent textbooks on social network analysis (in particular, Wasserman and Faust 1994; Scott 2002; Carrington, Scott, and Wasserman 2005; de Nooy, Mrvar, and Batagelj 2005) provide excellent introductions to methodology and to the key problems of social networks, but do not keep track (or could not yet keep track with, because they were written earlier) with the rapid extension of network science.
For the rapid extension, primarily physicists and scholars of complex systems are responsible. Popular books that provide a comprehensive overview on these traditions and published in the last decade (Barabási 2002; Newman, Barabási, and Watts 2006), however, do not sufficiently build on the long tradition, advanced methods, and wide range of interesting findings of social network analysis.
Common in both disciplinary approaches is the neglect of economic applications and structured interdependencies (Watts 1999 is to a certain extent an exception). Economics and game theory with the study of networked markets, games embedded in networks and network formation turned their interest toward networks in the 90'ies. Matthew Jackson is the most prominent researcher working in the field of games and networks. Hence, it is not surprising that he is able to bring this line of research in level with the two traditions dominated by sociologists and by physicists.
Structured interdependencies are particularly important for researchers conducting agent-based simulation, hence for most readers of JASSS. Actions of agents are interdependent and interdependence might vary depending on ties between agents. This should be present in models and simulations of social phenomena and constitutes the main reason why Jackson's book could provide fruitful insights for readers of this journal.
More importantly, this is the first book that integrates traditions of different disciplines and gives a comprehensive introduction to social and economic networks. The main focus is on models and on the "theory behind the structure, formation, and implications of social networks" (p. xi). This implies that the book fits more to traditions that emphasize modeling the network structure than to the empirical tradition on social networks. On the other hand, although only to a less extent, sections are devoted also to social problems related to networks, such as power and inequality, social capital, and the problem of embeddedness.
In particular, emphasis is on random graph models and on game theoretic models of networks. The duality of models based on random graphs and game theoretic models is preserved and paralleled through the entire book. The book has a progress starting from elementary concepts in these two traditions and becomes more technical and specific through the book.
On the other hand, this structuring goes along with another that divides the book into four main parts:
2. Models of network formation
3. Implications of network structure
4. Empirical analysis of networks.
Part 1 provides a compact and up-to-date introduction to social and economic networks and represents a very nice example of integrating different traditions. Part 2 displays a comprehensive overview on models of network formation in a formal style. This style, however, remains highly accessible for the general reader and is far from displaying the usual high mathematics of game theory texts. Part 3 is just a selection of implications, but picks the ones of general interest and applicability: innovations and diffusion; opinion dynamics and learning; games and decisions; and networked markets. Part 4 is at least as much about game theoretic models as about "empirical analysis", and contains many research hints for the agent based modeler. As the progress in the two modeling traditions is disrupted by another structuring, there are some repetitions (e.g., the term "scale-free" is explained on p. 31 and again on p. 61), but this is necessary for readers who would like to use the book as a source of reference.
Examples in the book are illustrative and come from the empirical social network analysis tradition as well as from physics and from game theory. The exercises at the end of each chapter are especially useful for those would like to have a deeper understanding of the chapter, although sometimes require advanced skills in mathematics.
To sum up, this is "a must-read for everybody interested in social and economic networks" (Barabási on back cover). And should you buy this book? If you do not buy it, you could freely borrow it from any of your friend colleagues, but not from their friends. If none of your friend colleagues have the book, it is better to buy it. Whether you can be a free-rider or not depends on the network structure. For your precise answer, just read the book.
CARRINGTON PJ, SCOTT J and WASSERMAN S (Eds.) (2005) Models and Methods in Social Network Analysis (Structural Analysis in the Social Sciences). Cambridge University Press: Cambridge.
DE NOOY W, MRVAR A and BATAGELJ V (2005) Exploratory Social Network Analysis with Pajek. Cambridge University Press: Cambridge.
NEWMAN M, BARABÁSI A-L and WATTS DJ (2006), The Structure and Dynamics of Networks. Princeton University Press: Princeton.
SCOTT J (Ed.) (2002) Social Networks: Critical Concepts in Sociology. Routledge: London.
WASSERMAN S and FAUST K (1994) Social Network Analysis. Methods and Implications. Cambridge University Press: Cambridge.
WATTS DJ (1999) Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press: Princeton.
Return to Contents of this issue
© Copyright Journal of Artificial Societies and Social Simulation, 2009