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To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic.
Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.
- Sales Rank: #1152136 in Books
- Published on: 2003-03-27
- Original language: English
- Number of items: 1
- Dimensions: 6.00" h x .60" w x 9.10" l, 1.30 pounds
- Binding: Paperback
- 432 pages
About the Author
Peter R. Monge is at University of Southern California. Noshir Contractor is at University of Illinois at Urbana-Champaign.
Most helpful customer reviews
20 of 22 people found the following review helpful.
Must read
By Aby Rao
I think is is a very good book and very effectively highlights the various socio-coginitve issues that are prevalent in communications. The theories could be applied to form new networks or to improve the strength of existing networks. The most prominent feature of the book is that it convincingly draws relationship between diffrent types of theories present in other disciplines ranging from Complexity theory to chaos theory. I wish the book had more illustrations and graphical examples. The format of the book is monotonous. But overall it certainly deserves 5 stars.
4 of 4 people found the following review helpful.
Good material, but needs a lot better organization.
By HelloWorld!
The book covers many interesting things, but the organization of the topics needs a lot of improvement.
Pros:
(1) Covers a lot of theories that you will find interesting, if you are working on network-related studies.
(2) Hands-on, step-by-step guidelines on how to use PSPAR program to do p* modeling.
Cons:
(1) The Table of Contents should be a lot more detailed. At least include the subsections. Every time I tried to find something that I read earlier, it takes a long time.
(2) Need more subsections within the chapters.
(3) Applying the ERGM model - I hope the authors have plans to update the book using more state-of-the-art programs like Statnet. PSPAR might be good but hasn't been updated for a long time.
Overall, I think it's good to keep as a reference. The MTML (Multi-thoeretical, multi-level) approach that they propose makes sense, but it just leaves you a feeling that they are trying to say a lot of things but didn't quite accomplish that. If they are stronger on the application part (i.e. how to actually model your data) with good software, it would have been much more helpful.
14 of 18 people found the following review helpful.
THIS BOOK IS THE GOLD STANDARD...
By A Customer
to which all other books written about network communications should be measured. Incredibly intelligent, thoroughly engagaging, this reader picked up this book out of curiosity, and was completely in awe of the complexity of networking. This book does a beautiful job of combining inferential statistics with dynamic theory. You will not want to put this down; this reader called in sick to work in order to finish the book. If you are going to own one book about networking, it should be this book. Seriously.
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