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Theories of Communication Networks Paperback – March 27, 2003

ISBN-13: 978-0195160376 ISBN-10: 0195160371

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Theories of Communication Networks + Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences) + Social Network Analysis (Quantitative Applications in the Social Sciences)
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Product Details

  • Paperback: 432 pages
  • Publisher: Oxford University Press (March 27, 2003)
  • Language: English
  • ISBN-10: 0195160371
  • ISBN-13: 978-0195160376
  • Product Dimensions: 9.1 x 0.6 x 6 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 3.7 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #215,847 in Books (See Top 100 in Books)

Editorial Reviews

About the Author

Peter R. Monge is at University of Southern California. Noshir Contractor is at University of Illinois at Urbana-Champaign.

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Customer Reviews

3.7 out of 5 stars
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Most Helpful Customer Reviews

20 of 22 people found the following review helpful By Aby Rao on May 28, 2003
Format: Paperback
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.
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14 of 18 people found the following review helpful By A Customer on April 11, 2003
Format: Paperback
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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6 of 7 people found the following review helpful By Aleksander Wawer on February 24, 2006
Format: Paperback
This book is a good description of various approaches to the wide topic of social network-related theories (not social network theories as in SNA).

All chapters (except the last) are a reasonable, literature-rich investigations drawing on different theoretical perspectives of "communication networks". The last chapter is where the authors try to put everyting together, and in my opinion partially fail in doing so. Conclusion is correct and intuitive, yet somehow trivial: thinking about communication networks requires multilevel and multitheoretical approach.

On the pros side: rich literature review, many sources cited. The content is dense.
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3 of 3 people found the following review helpful By HelloWorld! on March 18, 2010
Format: Paperback Verified Purchase
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.
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