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*****  To join INSNA, visit http://www.insna.org  *****

Thank you for all the very friendly and welcoming recommendations!

My lab at UAB has a very large library internally that is mostly comprised of my inability to resist buying books.  We'll be growing the Social Network part of that library quite nicely thanks to all of you.

I'm sure I'll have more questions as I begin exploring the various resources that have been recommended.  Is this list suitable for basic questions or would that be an inappropriate use of the list?

Thanks again for all the great recommendations.  The Amazon orders have been placed on the PDFs are being downloaded . . . 



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Gary Warner
Director of Research in Computer Forensics
The University of Alabama at Birmingham
Center for Information Assurance and Joint Forensics Research
205.422.2113
[log in to unmask]

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----- Original Message -----
From: "Kathleen Carley" <[log in to unmask]>
To: "Gary Warner" <[log in to unmask]>
Sent: Tuesday, July 28, 2015 7:02:15 AM
Subject: Re: Social Network Analysis for Beginners??

Gary
  hi
  ORA is very easy to use.  You might want to take a look at it as it
supports 2 mode data very well.  Since your data is already in Pajek
format you can import Pajek files directly into ORA using the wizard.
ORA is free for all faculty and student at universities and can be
downloaded at http://www.casos.cs.cmu.edu/projects/ora/

On 7/27/2015 5:21 PM, Gary Warner wrote:
> *****  To join INSNA, visit http://www.insna.org  *****
> 
> I wonder if the group could help a newcomer with advice on some fundamental tasks, like a "Getting Started with Social Network Analysis"?
> 
> I have been gathering information about a certain group of social media users and which ones of them follow one another.   We have about 15 million individuals in this network which has very high connection groups within the overall network (72 million connections between them).  We would like to use some "well accepted" methods of finding core subgroups within this meshed network, but I have to confess, while I have done tons of data mining, I've never really done "Social Network Analysis" in a graph-theory type way.  I have many years experience analyzing primarily the structure of computer networks, malware, and botnets, and the criminals who run them, but this data set is far beyond my experience and I feel I need more proven tools and techniques to address it properly.  My old techniques seem to run out when I get much above 100,000 connections.
> 
> Any suggestions on good "getting started" books, articles, or papers would be most helpful.
> 
> I've loaded a subset of this data into Pajek ... it represents 8913 social media accounts with 3.9 million connections between them, but realize that I need some theoretical background in Social Network Analysis to really even understand the range of "What might I do next" possibilities.
> 
> My objective is to be able to find "strongly connected" and "weakly connected" members, and to determine if there is a connectivity threshold where the network would "split" into two or more clusters.
> 
> I would be grateful to the group for any suggested reading.  Not afraid to read!  I think I need a bit of basic theory before jumping in to the Pajek manual though.
> 
> As Thomas Jefferson wrote in 1769 to Thomas Turpin Shadwell:  "the only help a youth wants is to be directed what books to read, and in what order to read them."
> 
> That's the main help I'm asking for right now.  The answer may well be RTFM (Read The Fine Manual), but the question is which M to F'ing R first?
> 
> Thanks for any opinions, and I apologize if I've just stumbled across a list FAQ question.  A pointer to same would be appreciated if that is the right way to get started!
> 
> 
> ----------------------------------------------------------
> 
> Gary Warner
> Director of Research in Computer Forensics
> The University of Alabama at Birmingham
> Center for Information Assurance and Joint Forensics Research
> [log in to unmask]
> 
> -----------------------------------------------------------
> 
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