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I agree with many of the recommendations. If you are mathematically 
inclined, and/or want to better understand the metrics, I would also add 
Newman's 2010 Networks 
(http://www.amazon.com/Networks-An-Introduction-Mark-Newman/dp/0199206651), 
as it helped me considerably as a researcher to understand the 
mathematics behind the methods. It may be too much for less 
mathematically inclined students.

In my teaching, I have used a combination of Barabasi's online book 
(http://barabasi.com/networksciencebook/, generally following this 
sequence for the semester), the ASU book (http://dmml.asu.edu/smm/, 
which in places is a distilled version of Newman and fills in some 
details Barabasi does not cover), and optionally SAND (Stastistical 
Analysis of Network Data) for those who use R/igraph, with occasional 
selections from another book that has not yet been mentioned in this 
thread: Easley & Kleinberg's Networks Crowds & Markets 
(http://www.cs.cornell.edu/home/kleinber/networks-book/). E&K are great 
at illustrating how the methods solve real problems in relation to 
theoretical issues (almost completely absent from Newman), but their 
coverage of methods is not sufficient to serve as a text in itself (and 
I did not attempt to include the game theory & market theory aspects). 
BTW my course used Gephi and igraph as they are available on multiple 
platforms and good at what they do.

If I were forced to dichotimize, I think there are two approaches: big 
picture approaches that analyze whole networks and are interested in 
recurrent phenomena across multiple domains rainging from genetics to 
the Web, with mathematical perspectives coming out of physics (Barabasi, 
Newman); and approaches that are more sociological in nature (hence 
emphasizing human networks) that are more (or also) interested in diving 
into the details of specific networks to identify central actors, roles, 
and community structure. I advertized my course across campus and had a 
great combination of students from many disciplines so needed to address 
multiple needs and interests with both of these perspectives. It was a 
fun course to teach and I learned a lot teaching it.

Dan

-- 
Dan Suthers

Dept. of Information and Computer Sciences
University of Hawaii at Manoa
1680 East West Road, POST 309, Honolulu, HI 96822
(808) 956-3890 office
http://www2.hawaii.edu/~suthers/

Professor, Department of Information and Computer Sciences
   http://www.ics.hawaii.edu/
PI, Laboratory for Interactive Learning Technologies
   http://lilt.ics.hawaii.edu/

Productive Multivocality in the Analysis of Group Interactions
Springer: http://bit.ly/1zJgzBl Synopsis: http://bit.ly/1qSdvlJ

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