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Hi everyone, I am doing a meso-level network analysis and trying to identify the subgroups in the network. After reading the chapter 11 of Analyzing Social Networks (second edition 2018), I want to combine the Clique analysis and HiClu so as to remove the overlap. Here is my procedure: (1) go to Network|Subgroups|Cliques to get the Actor-by-Actor Clique Co-Membership Matrix; (2) Given that the output of Clique analysis- HIERARCHICAL CLUSTERING OF OVERLAP MATRIX doesn’t report the Q value (see the zache example in the word attachment), I run the HiClu analysis (Tools|Cluster Analysis|Hierarchical) based on the Co-Membership Matrix. And the participation that maximizes the Q value is used to identify the subgroups in my study. Am I right? Besides, I noticed that the hierarchical clustering results of these two methods are different (please see the two figures in the word attachment). Does anyone have a clue about the differences? Any information would be appreciated. Thank you! Best, Longxia Huo Ph.D. Student

Hello everyone: I want to compare the position of a set of variables in two correlation matrices. Basically are the same variables measured in two different samples and what I want to know is if each variable has a similar or different profile of correlations with the rest of variables in those two populations. What would be the procedure to follow with UCINET? I have tried with Role & Positions>Structural>Profile, using a join matrix with the two correlation matrices, but it does not seem to give me results for the diagonal, where it indicates that all the actors have a perfect structural equivalence (value of 0 using the Euclidean distance). Although maybe I'm not applying the routine well... Thank you in advance. Bye! ----------------------------------------------------------------------- Daniel Holgado Ramos Laboratorio de Redes Personales y Comunidades (HUM-059) Departamento de Psicología Social Universidad de Sevilla Tlfnos.: +34 954557708-666882919 dholgado@... -----------------------------------------------------------------------

Hi all, this is to announce a set of online workshops offered this June by the University of Kentucky’s LINKS Center for Social Network Analysis. The website for the 2023 workshops is www.linksworkshop.org. Introduction The LINKS Center for Social Network Analysis at the University of Kentucky offers a series of workshops on social network analysis (SNA) every summer, usually in June. This year we are offering four synchronous online workshops (conducted over Zoom). Each workshop meets for 3 to 5 days for about four hours a day (not including optional extra sessions in some of the workshops). The workshops and dates are shown below. In addition, workshop participants will also have access to a limited number of non-technical one-on-one consultations with experienced research faculty to discuss your individual project(s). Track 1. Introduction to Social Network Analysis - May 31 - June 2, 2023 (9:30-11:30am and 12:30-2:30pm, ET) Led by Dan Brass, Rich DeJordy and Dan Halgin, this course provides a basic introduction to the theory and method of network research. Topics include theoretical perspectives, social capital, and the nuts and bolts of doing a network analysis using UCINET and NetDraw software (both Windows programs). Starting June May 31, it meets Wednesday, Thursday, and Friday from 9:30am-11:30am ET (New York time) and 12:30pm-2:30pm ET (12 contact hours) and provides homework assignments and access to TAs. The course costs $1500 ($750 for students). More details ... Track 2. Intermediate Social Network Analysis (UCINET) - June 5-9, 2023 (10:00-12:00am and 12:45-2:45pm, ET) Taught by Steve Borgatti, this is a more technical and in-depth workshop focusing on the concepts and methods of SNA, with particular emphasis on relating network measures to research goals. The mathematics behind the measures is explained, as well as how to use the measures in a research context. UCINET for Windows software is used extensively. Although we don’t use R in this workshop, the course is closely tied to the book Analyzing Social Networks Using R by Borgatti, Everett, Johnson and Agneessens (2022). The course meets Monday through Friday June 5-9 in two daily sessions 10:00am-12:00pm ET and 12:45pm-2:45pm ET. The fifth day is led by the TAs. The course costs $1800 ($900 for students). More details ... Track 3. Intermediate Social Network Analysis (R) - June 5-9, 2023 (10:00-12:00am and 12:45-2:45pm, ET) Taught by Filip Agneessens and Francisco Trincado-Munoz. Like Track 2, this is a more technical and in-depth workshop than the intro workshop, focusing on the concepts and methods of SNA, particularly as they apply to specific research objectives. This workshop uses the R package xUCINET, rather than Windows UCINET. Basic familiarity with R is helpful but not required. The mathematics behind the measures is explained, as well as how to use the measures in practice. The course meets five times starting June 5 with each instructional day consisting of two sessions: 10:00am-12:15pm ET and 12:45pm-3:00pm ET. Participants will receive homework, which includes running analyses and interpreting results, which they can perform in small groups of 2 or 3. These results are then discussed at the next meeting. The course is closely tied to the book Analyzing Social Networks Using R by Borgatti, Everett, Johnson and Agneessens (2022). The course costs $1800 ($900 for students). More details ... Track 4. Stochastic Models of Networks - June 12-16, 2023 (10:00-12:00am; 12:45-2:45pm; and 3:30-5pm, ET) Taught by Robert Krause, this course covers ERGMs and SAOMs, two families of statistical models used to model the presence or absence of ties. While the course is introductory, prior familiarity with both statistics and network analysis is strongly advised. The course aims to be interactive, using breakout sessions for the exercises and time between classes to consolidate knowledge. Participants will receive homework involving running further analyses and interpreting result. The homework can be performed in small groups of 2 or 3. These results are t

Hi all, a friend just asked me whether UCINET and NETDRAW run well on Macs using Rosetta. I had never heard of Rosetta, but some Googling suggests that it is nothing short of amazing. You don’t have to buy it, you don’t have to buy Widows, and you don’t have to explicitly run it – the Mac detects intel-based software and uses Rosetta to run it. https://en.wikipedia.org/wiki/Rosetta_(software) Can it be true? Has anyone run Ucinet/Netdraw on Rosetta? steve Stephen P. Borgatti Gatton Endowed Chair Dept. of Management University of Kentucky

Dear, I am searching for information about ARD (avg recip dist), a node-level measure from UCINET. Do you have any ideas where I can get them? In the Help Topics couldn't find them, though. I really appreciate any thoughts on it. Regards, Stela

Hi, Steve, I downloaded the new UCINet version – and things are still weird. It seems to relate to saving the NetDraw network as a vna file and then trying to use the saved vna file. I can use Netdraw to open a UCINet data set and things seem to work OK. The set of networks opens with double arrowheads and I can turn them all off and on. When I have a configuration I like, I want to save it for future manipulation. So I save it as a vna file and shut down NetDraw. It’s when I open the vna file with Netdraw (the built in link from the UCUNet top banner) that things get weird. The networks open, again, with double arrowheads. But when I try to turn the arrowheads off, only a very few of the smaller networks lose their arrowheads. The others retain them. I try opening a different vna file, saved from the older version of UCINet. Same thing. Tried opening a vna file from about 5 years ago – same thing. There must be something still wonky with respect to NetDraw and the arrowheads – I don’t think 15 years of UCINet vna files have just gone round the bend. Could you look into this? Kate McCain

Hello, I'm a teaching assistant in a graduate level social network analysis class where we use UCINet. Many of the students have newer M1/M2 Macs and are unable to install UCINet via VirtualBox since it's still in beta for the silicon macs. We are aware that they could purchase and run UCINet using Parallels, but we feel that it is unfair to ask certain students to purchase Parallels and the university will not supply licenses... Has anyone successfully run UCINet on an M1 Mac, NOT using Parallels? Any suggestions or advice would be much appreciated, thank you! -Sarah

Is there a option for regular logistic regression using permutation tests in UCINET? (E.g., at the Node level?) (Like there is for OLS.) (It might be useful for some of us.) I don't see one in the version of UCINET I have. Though, of course, there is one for the QAP version at the dyadic level. Also, while I'm here, on my version (6.755) the help button for LR-QAP is not linked to the right help item (it is linked to DOUBLE DEKKER SEMI PARTIALLING MRQAP). Sincerely, David Tindall -- David Tindall Professor Department of Sociology, University of British Columbia Chair Environment and Society Minor, Faculty of Arts, University of British Columbia Mailing address: Department of Sociology University of British Columbia 6303 N.W. Marine Drive Vancouver, British Columbia Canada V6T 1Z1 Office Location: Anthropology and Sociology Building Room 1317 E-mail: tindall@...

The independent variable "betweenness centrality score" in my data is ranged between 0 and 4.79 (on continuous scale) for undirected nodes, whereas the dependent/outcome variable "infection status present/absent" in my data is binary i.e., 0 or 1. I performed elastic net regression to estimate the effect size and found a negative coefficient value. Could someone from the group help me how to interpret this result? Thank you Shovon

Hi everyone, I hope you are doing well. When I used UCINET to test a node-level hypothesis, something went wrong. My dependent variable is centrality, and my independent variables are some dummy variables and some continuous variables. When I include all dummy variables, all Beta, T, and c.Sig disappear, whereas all p.Sig are 1. When I reduce one dummy variable, it becomes normal. Could anyone explain how this happens? Thanks. Best, Simon

Dear all, I used to analyze social network data with UCINET. And recently I am trying to use the xUCINET package to do the analyses considering its flexibility. Now I have a question about imputing missing data with xUCINET. According to Borgatti, Everett, and Johnson (2018): “In the case of symmetric or undirected relations, a simple cure is to fill in any missing rows with the data found in the corresponding column. The assumption is that, if the respondent had been able to answer, they would have listed all the actors that mentioned them. This may not be exactly right, but it will be more accurate than treating the missing values as zeros. UCINET has a command called REPLACENA within Matrix Algebra to do this.” However, in the current version of xUCINET, the xImputeMissingData() function only imputes data with density. I am wondering whether we can manually write some codes in xUCINET to realize the function of REPLACENA in UCINET? If yes, could you please give me an example? Many thanks! Best, Chuding

I've been having some odd interactions with Netdraw--both when I open a UCINet data file or the original dl file. The original files themselves are fine --UCINet likes my dl file. I can open both data formats in Netdraw and work with the results--rearranging the individual networks for visibility, etc. When I first open a file, the links have arrowheads at both ends. No problem -- I turn them off. However, if I save the Netdraw data for that configuration as a vna file, when I reopen it later on, I have arrowheads on the links that will not turn off. It's frustrating. Any thoughts? It's a PITA to have to open a new network from the original data and redo all my work every time I want to tweak something (e.g. color/shape of nodes, vary tie size visualization, you name it). Thanks in advance. Katherine W. McCain, Professor Emerita Department of Information Science College of Computing & Informatics Drexel University

Dear all, I’m about to use geographical coordinates in to create network in Ucinet. This is my first time and I have a very basic question Should I only include numbers when entering the coordinates to Ucinet? For example: Latitude = 33.9173° S and Longitude = 151.2313° E - should the °, E and S be deleted? If so will I need to entre + or – to indicate the direction? Thank you, Jordan Dr Jordan Soukias Tchilingirian Lecturer/Assistant Professor Political Science and International Relations, School of Social Sciences, Room 2.64 ? M257, Perth WA 6009 Australia T +61 8 6488 7239 ? E jordan.tchilingirian@... Selected publications Think tanks and policy intellectuals Tchilingirian, J.S (2021) ‘Network intellectuals’ and ‘Networked Intellectuals’: Relational approaches in the study of British think tanks. In D. E. Abelson & C. Rastrick (Eds.), Handbook on Think Tanks in Public Policy. Edward Elgar. Tchilingirian, J.S (2018) Producing Knowledge, Producing Credibility: British Think-Tank Researchers and the Construction of Policy Reports. International Journal of Politics, Culture, and Society, 31(2), 161–178. https://doi.org/10.1007/s10767-018-9280-3 Global Development Partnerships Faul, M. V., & Tchilingirian, J.S. (2021) Private sector representation, contribution and potential influence in global financing partnerships. Development in Practice (online first) https://doi.org/10.1080/09614524.2021.1938514 Faul, M. V., & Tchilingirian, J.S. (2020). Structuring the Interstitial Space of Global Financing Partnerships for Sustainable Development: A Network Analysis. New Political Economy, 1–18. https://doi.org/10.1080/13563467.2020.1849082

Hi all, I encountered a problem when running MRQAP. I try to investigate which factors influnce formal collaboration between cities(weighted and undirected). I have two independent variables: IV1-informal interaction (weighted and undirected); IV2-network of prescribed interaction (binary and unweighted), and one control variable. When I include IV1 and the control variable, IV1 is insinificant and sign is +. However, when IV2 is included, IV1 is significant and the sign is -. I guess that issue may arise due to colinearity between IV1 and IV2. Is there a solution like the residualization strategy in OLS to sovle this problem? (maybe it sounds strange╯︿╰) Any information would be appreciated. Thank you! Best, Longxia Huo Ph.D. Student