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Dear Jay,

I would suggest considering a bivariate ERGM. This can be estimated with
pnet.
Look in google scholar for
bivariate ergm pnet
and you shall find various references.

Best wishes,
Tom

=========================================
Tom A.B. Snijders
Professor of Statistics and Methodology, Dept of Sociology, University of
Groningen
Emeritus Fellow, Nuffield College, University of Oxford
http://www.stats.ox.ac.uk/~snijders

On Sun, Jan 8, 2017 at 10:49 PM, Hegde, Jay <[log in to unmask]> wrote:

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> -----------------------
> Sender:       Social Networks Discussion Forum <[log in to unmask]>
> Poster:       "Hegde, Jay" <[log in to unmask]>
> Subject:      How to compare two directional networks of the same size?
> ------------------------------------------------------------
> -------------------
>
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>
> Hi Everyone,
>
> We have two NxN connectivity matrices that define weighted, directional
> int=
> eractions among the same set of N nodes under two different conditions, A
> a=
> nd B. Since the interactions are directional, our connectivity matrices,
> wh=
> ile being square, are asymmetric.
>
> We want to identify the interactions that are significantly different
> betwe=
> en the two conditions.  That is, how do the nodes talk to each other under
> =
> condition A that do not do under condition B?  Of course, we want to do
> mor=
> e than just calculating the difference between the two matrices. For one
> th=
> ing, we know that if the matrices are symmetrical, there are toolkits for
> c=
> omparing them.
>
> But we're not sure how to compare our asymmetric matrices. Any ideas about
> =
> (1) how to do it, and (2) any toolkits that implement the solution?
>
> Thank you very much,
> Jay
>
> Jay Hegd=E9 PhD
> Medical College of Georgia
> Augusta University (formerly Georgia Regents University)
> Augusta, GA 30912-2697
>
>
>
>
> _____________________________________________________________________
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> <p class=3D"MsoNormal">Hi Everyone,<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal">We have two NxN connectivity matrices that define
> <b=
> ><u>weighted</u></b>,
> <b><u>directional</u></b> interactions among the <b><u>same set of N
> nodes<=
> /u></b> under two different conditions, A and B. Since the interactions
> are=
>  directional, our connectivity matrices, while being square, are
> <b><u>asymmetric</u></b>.<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal">We want to identify the interactions that are
> signif=
> icantly different between the two conditions.&nbsp; That is, how do the
> nod=
> es talk to each other under condition A that do not do under condition B?
> &=
> nbsp;Of course, we want to do more than just
>  calculating the difference between the two matrices. For one thing, we
> kno=
> w that if the matrices are symmetrical, there are toolkits for comparing
> th=
> em.<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal">But we&#8217;re not sure how to compare our
> asymmetr=
> ic matrices. Any ideas about (1) how to do it, and (2) any toolkits that
> im=
> plement the solution?<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal">Thank you very much,<o:p></o:p></p>
> <p class=3D"MsoNormal">Jay<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal">Jay Hegd=E9 PhD<o:p></o:p></p>
> <p class=3D"MsoNormal">Medical College of Georgia<o:p></o:p></p>
> <p class=3D"MsoNormal">Augusta University (formerly Georgia Regents
> Univers=
> ity)<o:p></o:p></p>
> <p class=3D"MsoNormal">Augusta, GA 30912-2697<o:p></o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> <p class=3D"MsoNormal"><o:p>&nbsp;</o:p></p>
> </div>
> </body>
> </html>
> _____________________________________________________________________
> SOCNET is a service of INSNA, the professional association for social
> network researchers (http://www.insna.org). To unsubscribe, send
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>

_____________________________________________________________________
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