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Hi I vaguely recall an error in the Davis data that was corrected and this could cause the change. Also we do update and improve routines and correct bugs and these too could make a difference. Martin

Hi Martin, Thanks! That’s really helpful. I tried the dual-projection method, following the steps mentioned in the document. There are indeed 5 core events now. But the women-by-women partition

Hi Dual core periphery in UCINET uses the categorical method whereas xUCINET uses the dual projection which is a continuous method. The later and usually better method is the dual projection. This can

Dear all, I’ve used UCINET to run Davis_SouthernWomen data, using 2-mode categorical core-periphery. As the attached output shows, the core events are 7, 8, and 9. But these results seem different

Sure. Suppose I construct the woman-woman 1-mode projection from the davis data, accepting the default output name of davisrows. Now, I run network|subgroup|fgroups on davisrows:

Hi, there is a bug. You can fix the problem by installing the "blockmodeling" package before installing the xUCINET package. In R Studio, use the install.packages function as follows: Then following

Dear all, I’ve tried to install xUCINET on RStudio, but failed. I followed the steps as suggested here: https://www.analyzingsocialnetworksusingr.com/xucinet. The error message is: ERROR: dependency

Is there a way to also dichotomize in UCINET via g-transitivity?

So far dichotomizing and exporting has worked fine, at least for 115 and 133 symmetric datasets I've tried and that includes success with finding triads and tetrads although that will take some time

Comparing my Bonich'72 starting data with the above I can see only one difference. My data has three digits past the decimal and your data is rounded to off to two digits past the decimal. Could that

Actually, I think I might have mixed up the cutoff with the correlation. But, btw, the numbers you are showing look slightly different from mine. A problem with the sorting? steve

1. I cannot still am not sure about the 0.816 value and although that is a good correlation, there are higher values. 2. Thank you Nelson, but I do not do network analysis on a consistent basis every

Hi Scott, I use UCINet for many network analysis, but for motif counting, characterizing and modelling I prefer working in R (i.e., igraph). Best of luck. Nelson Jatel, Doctoral Candidate Nelson R.

Thank you. Actually this approach to dichotomizing is forcing me to reassess my method which was assigning 1 to mean +/- 1SD and all else a zero with the recognition that low degree individuals are

Dear UCINET community, The LINKS Center for Social Network

I followed the procedure described at the bottom of page 5 in this paper: https://uknowledge.uky.edu/management_facpub/5/ The technique is implemented in ucinet's 'Transform|Dichotomize interactively'

Ok. Looks good, but only one question. Why choose 0.816 as the dichotomize value? Wouldn't 0.50 be acceptable or perhaps the mean value of the matrix?

This software could be very useful. Thanks for bringing it to our attention. An advantage of the UCINET data export is that you can vary the minimum tie value to focus on the more strongly connected

Scott, your mfinder program wants non-valued data (it's trying to count up things like triadic configurations). So, you would need to dichotomize your valued 1-mode data. For example, suppose I have

I want to to find network motifs (primarily triads and tetrads) and identify their frequencies, Z-scores, and memberships. I am trying to follow the work of Milo et al. (2002) Network Motifs: Simple