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Hi I calculated how much type I relations correlate (QAP Correlation) with type II relations for the ego network, of course, noting that the type I egonet should contain the same nodes as the type II egonet. To do this, I performed the union of the nodes of each egonet to ensure that the same nodes would be present in both egonets. The results are in the attached csv. The egonets are directed and valued. I expected inverse CorrCoefs; however, all calculations related to the inverse presented a p-value > 0.05. If it had occurred with some cases, I would understand. But all? Question: It seems that QAPCorrelation may have been designed for direct correlations. And that suits me. If true, then I should have symmetrized (and perhaps dichotomized) the networks. Is that correct? matui

I find that E-NET does not have as much difficulty with my datasets as UCINET when doing ego analyses. It is somewhat slow but does a good job (197x205 dataset done in 80 minutes). I did a Save at the end and four E-NET Database files were created, i.e., Output.alters, Output.alterties, Output.egos, and Output.enet. My question is simple. How does one reload those files into E-NET as so far it just wants to load or import new data files? Thank you.

I need to dichotomize four attributes in larger than typical datasets that I normally utilize, i.e., 100-200 cases (rows) but now need to analyze data of 2100 x 200 with UCINET but first want to dichotomize four continuous attributes before proceeding. So I reduced the dataset to just 2100 x 4 attributes (only the continuous ones), converted to 1-mode, and then tried Dichotomize Interactive to look at the correlations. After 18 hr of running with the spinning wheel in UCINET I gave up and repeated with only 2 attributes with same problem. I am running Windows 10 64bit with 64GB RAM (although UCINET 32bit seldom uses more than 4GB) with AMD Ryzen CPU, dual fast SSDs, and a good graphics card. I looked to see if Pajek if could do this but did not see that option. For now I using an a priori approach which I feel is not optimal but works, i.e., forcing the creation of only 2 clusters via different methods. I find that mixing continuous data with binary gives distorting results based on the scale of the continuous data after sums-squared reduction (even with normalizing and with better scale matching). Any ideas would be appreciated otherwise I will continue with the a priori method that was covered by Steve Borgatti in the excellent technical document ((https://uknowledge.uky.edu/management_facpub/5/). Thanks.

Hello! I'm trying to use 'Johnson's Cluster Analysis' from the Tools menu on my dataset in order to perform a Hierarchical Cluster Analysis. I've tried messing around with some options in the menus, also I'm using the 32-bit version, but I've not been able to get it to produce the specified dendrogram. It produces a .txt file with lots of information and results, however, at the very bottom of the file it says "Graphical dendrogram suppressed." Why would it be suppressing my graph? Are there any potential error logs I could check to see why its doing this? I have tried messing around with basically every option in the menu, to no avail. Even using different datasets has all resulted in the dendrogram being suppressed.

Hi, I have the dataset with 6k nodes with attributes. The attached file is just an extraction as an example of attributes. I want to define an attribute-based effect for the running analysis. Is there a tutorial to help you prepare the data? saluti m

Hi - very new user here! How can I view or download the parameters used to create a VNA file? Also, is it possible to open a VNA file and swap the data so that the same diagram parameters can be used on a different dataset? Thanks, David

Hello fellow networkers, I’m not sure what I did, but now the default ‘notepad.exe' helper app is not working. The app itself is works fine if it is not called for by ucinet to display an output (i.e. when I open a .txt file directly). When I run any ucinet routine that would generate an output nothing happens; the output files are created in the declared default folder, but no text visualization is given. I have selected notepad++ as the default helper app for the text editor as a temporary solution (which is working fine) but would like to have the windows notepad.exe app be the default as customary. Any ideas what I might have screwed up? I have updated to the newest version of ucinet (6.800), but the bug was not fixed. Thanks in advance for your help … Jorge

Dear Network Community, see below a call for papers for Connections. We are looking for high quality papers that wouldn’t find a home in traditional journals because of their topic or their format, but are of real interest to the network community. Editors Dan Halgin and are at the Sunbelt conference in Portland if you would like to talk to us about a possible submission. Thanks, Steve & Dan Connections - An INSNA Journal Call for papers Do you have ideas for a network paper that might not fit in traditional journal outlets? Perhaps an early stage idea, a highly focused essay, a discussion of best or novel practices, a review of a statistical package, an opinion about the future of the field, a novel methodology, etc. We encourage you to consider Connections. Some of the most impactful work appearing in Connections has fit this mold. See, for example, Freeman (1996) on the history of network analysis; Batagelj and Mrvar (1998) introducing the Pajek software package; Borgatti, Jones and Everett (1993) on defining social capital; Krebs (2002) analyzing terrorist networks; Carley. Lee and Krackhardt (2002) on destabilizing networks; Park (2003) hyperlink analysis; Kapucu (2005) on emergency response networks; Kleinbaum and Quintane (2011) on the merits of email data; and Healey (2017) on governance. Guide for authors Generally, we are interested in conceptual, empirical, methodological, application or teaching-based manuscripts related to social networks. This includes shorter methodological or empirical notes that would benefit the field as well as ideas that are early in their development. Submissions Please submit your work using the online manuscript management system https://www.editorialmanager.com/connections/default.aspx On acceptance, Connections articles are made available online directly. Articles are not normally packaged into issues. We are accepting submissions now and until further notice. About Connections As an official journal of the International Network for Social Network Analysis, Connections aims to reflect the ever-growing and continually expanding community of scholars using network analytic techniques. The research spans many disciplines and domains including: Anthropology, Sociology, Psychology, Communication, Economics, Organizational Behavior, Knowledge Management, Marketing, Social Psychology, Mathematics, Public Health, Medicine, Computer Science, and Policy. This journal provides immediate open access to its content under the Creative Commons CC BY 4.0 license. Connections is a peer-reviewed journal. Further information Please contact Dan Halgin or Steve Borgatti for more information. References Batagelj, V., & Mrvar, A. (1998). Pajek-program for large network analysis. Connections, 21(2), 47-57. Borgatti, S. P., Jones, C., & Everett, M. G. (1998). Network measures of social capital. Connections, 21(2), 27-36. Carley, K. M., Lee, J. S., & Krackhardt, D. (2002). Destabilizing networks. Connections, 24(3), 79-92. Freeman, L. C. (1996). Some antecedents of social network analysis. Connections, 19(1), 39-42. Healey, P. (2017). Transforming governance: Challenges of institutional adaptation and a new politics of space. In Connections (pp. 357-374). Routledge. Kapucu, N. (2005). Interorganizational coordination in dynamic context: Networks in emergency response management. Connections, 26(2), 33-48. Krebs, V. E. (2002). Mapping networks of terrorist cells. Connections, 24(3), 43-52. Park, H. W. (2003). Hyperlink network analysis: A new method for the study of social structure on the web. Connections, 25(1), 49-61. Stephen P. Borgatti Gatton Endowed Chair Dept. of Management University of Kentucky

Dear networkers, I have a simple but fundamental concern with respect to DL type format. And I'm not sure if/how to solve it using either and/or edgelist and nodelist. Say I have three nodes that are linked among each other, only once. They form a perfect and undirected triad. E.g.: Pedro Juan Diego 1) How should I enter the data to represent the case in DL type format as a connected triad? 2) Would that be nodelist or edgelist? 3) If it's edgelist: which "data format" should I choose when importing to UCINET? Or the only way to solve this is as follows?: Pedro Juan Diego Juan Thank you in advance

Dear all, I hope this email finds you well. I am working on a hypothesis that "the degree centrality of all nodes will change significantly during the T1 or T2 stages of the NGO's mention network." I have already measured centrality data for all nodes in both stages. While a paired t-test would be the obvious approach if the data were independent, I am not sure how to account for the dependencies inherent in network data within UCINET. So far, I have two possible approaches in mind: 1) Import the centrality data from the two stages into SPSS and conduct a paired t-test. 2) Use UCINET's Tools > Test Hypotheses > Node-Level > T-Test function. I would greatly appreciate any suggestions or advice on how best to proceed with this analysis. Thank you so much for your time and assistance in advance. Best regards, Yvonne

Hello All, I am performing a Weighted Correlation Network Analysis (WCNA) where the correlation coefficients across a set of variables are used as the tie strength for their correlational network. I have performed a Principal Component Analysis on the data (in SPSS) to get the factor structure of the variable set. The PCA result shows that the 31 variables can be grouped into 8 components. Now I want to run UCINet to perform the following: Visualize the network with the 31 nodes categorized (different shapes) as per their component affiliation (obtained through the PCA) Density by group analysis by creating a block model of 8 subgroups of the variables checking the intergroup densities. I understand that a partition file needs to be created with the 31 variables and their respective attributes. Somehow, I am unable to execute the process. Please help me with the steps! Thanks in advance, Aritra.

Hi all, The Department of Management in the Gatton College of Business and Economics at the University of Kentucky seeks applications to fill one tenure-eligible position at the Assistant Professor or Associate Professor level (Regular Title Series, 9-month Assignment Period) beginning August 1, 2025. Research agenda is open, and teaching load typically consists of three courses per year. Eligible candidates must hold a Ph.D. in management or a related field. ABDs who expect to complete their degrees by August 2025 may also be considered. The position may also involve other duties such as curriculum-building, assisting with program management, and mentoring students. The courses and other assigned duties will be determined by the Department Chair based on the candidate's interests and areas of expertise. The assignment may be adjusted annually based on needs and performance. The Department of Management is home to the LINKS Center for Social Network Analysis which attracts international researchers from a wide variety of disciplines. We seek scholars who can contribute to and enhance our research profile and visibility. In addition to a vibrant undergraduate major and strong doctoral program, the department also offers a Master of Science in Strategic Human Resource Management and Analytics and contributes courses to the College's MBA programs. Compensation is highly competitive and includes an attractive package of benefits. You may view our full range of employee benefits by visiting the UK employee benefits web site: http://www.uky.edu/HR/benefits/ For additional information about the position, please contact Professor Walter Ferrier (walter.ferrier@...), chair of the Department of Management. We encourage you to visit our department website: http://gatton.uky.edu/faculty-research/departments/management. Applications must be submitted via the University's jobs portal ukjobs.uky.edu/postings/546007 and must include a letter of interest (upload under cover letter), a current CV, and the names and contact information for at least three references (used to solicit recommendation letters in advanced rounds of the search process). Applications will be reviewed immediately, but the position will remain open until filled. steve Stephen P. Borgatti Carol Martin Gatton Chair of Management Gatton College of Business and Economics University of Kentucky

HI, Maybe I'm looking wrong, I'm not finding the help file for LR-QAP in general. Ok, you test one way or another and in the end, you can understand the best use of the resource. However, the relational effects are not easy to interpret without a help file. Somewhere in this forum, I found: ? Reciprocity effect – If j likes i at T2, then predict that i will like j at T3 ? So create matrix X1 in which X1(i,j) = 1 if T2(j,i) = 1 ? Transitivity effect (closure) – If, at T2, i likes j and j likes k, then predict that at T3 i will like k. ? Create matrix X2 where X2(i,j) = 1 if (T2(i,j) =1 and T2(j,k) = 1)* ? Preferential attachment effect – If T2 indegree is high, predict that all nodes will want to have a tie – X3(I,j) = indegree(j) Can anyone enlighten me if the logic of the resource is as described above? tnx, p

Dear Prof. Borgatti and all, I am a reader of your book “Analyzing social networks using R” (Borgatti et al., 2022). Recently I notice that there is a stream of literature called node-weighted centrality or hybrid centrality (Singh et al., 2020; Singh, 2022). This literature points out that previous measures of centrality account for weight on edges (i.e., valued network) or length (e.g., eigenvector or beta centrality). However, these previous measures treat nodes as uniform. But it is very likely that different alters which an ego connects with are different in terms of a particular attribute (i.e., has different levels of importance). Let me use an example from Singh et al. (2020) to further illustrate: For a friendship network, nodes represent persons and edges represent the friendship relationship between the considered set of persons. Here, weights on the nodes can be understood as a mapping of wealth, power, education level, or some other attribute of persons. It is notable that existence of two persons with identical attributes is highly unlikely and therefore all person’s attributes can be mapped to different real values based on the application specific mapping. Therefore, my first question is whether we can account for such weights of nodes when calculating different types of centrality. Of note, I have quickly gone through the literature on node-weighted centrality or hybrid centrality, including 18 methodological or statistical research cited by Singh and colleagues (Singh et al., 2020; Singh, 2022), though I do not fully understand the mathematical parts. However, it seems that all of them use another type of centrality as the weight when calculate a particular type of centrality. For example, they use betweenness centrality as the weight when calculating degree centrality. From this perspective, your work on PN centrality might be also understood as a hybrid centrality (Everett & Borgatti, 2014). Particularly, in Everett and Borgatti (2014), you state: Note that this equation weights everyone the same, but in many cases we might prefer a measure in which actors with high centrality affect the centrality score of the actors they are connected to more than those with a low score. In other words, we might want to capture the notion that to have negative connections with actors who have a low centrality score is better than having negative connections to actors who have a high score. Nevertheless, all the literature aforementioned do not fully capture the notion in the example from Singh et al. (2020) that I cited. That is, they use relational attributes to obtain the weight. Therefore, my second question is whether we can use individual-level attribute like wealth, educational level or personality variables to indicate the weight. Related to the PN centrality in Everett and Borgatti (2014), I have the third question. That is, whether we can multiply the positive network and negative network using the matrix multiplication function in R first, and then calculate a particular type of centrality to obtain the weighted centrality. According to your example in Chapter 2 of Borgatti et al. (2022), maybe we can indicate the first matrix as the negative network and the second matrix as the positive network, then the product of the two networks indicates the extent to which an ego is negatively connected to alters who are positively connected to others. Therefore, conceptually it may also capture “the notion that to have negative connections with actors who have a low centrality score is better than having negative connections to actors who have a high score.” However, after experimenting with the example data provided in PN centrally section, I find the results are significantly different. Nevertheless, my fourth question is whether we can also understand the centrality based on the product of two matrix as one form of hybrid centrality. To recap, I list the four questions as follows: 1. Whether we can account for weights of nodes when calculating different types of centrality, especially in population software like

Hello, About xQAPLogisticRegression(), I can't find it in xUCINET version 0.0.2.0020. However, in the 2022 edition of the book 'Analyzing Social Networks Using R', there are even exercises. Are there any patches or scripts I should consider? matui

Hi all, Below is a call from Jan Fuhse for contributions honoring the work of Harrison White. Papers will be published in the INSNA journal of CONNECTIONS. The primary focus is on short, personal pieces and reflections rather than full-blown research papers (even though these might also be of interest). Ideally, we would have a wide range of contributions reflecting the different sides of Harrison's work and his far-reaching impact on a number of fields and trends. If in doubt or if you're undecided what to write, please contact Jan Fuhse <jan.fuhse@...>, who is serving as guest editor for this series of contributions. steve --------------------------------------------------------------- ?Call for short pieces in honor of Harrison White for Connections (official journal of the International Network of Social Network Analysis; https://www.insna.org/connections) Eminent networks scholar and 1984 Simmel awardee Harrison Colyar White passed away on May 18, 2024, in his Tucson home aged 94. As an early convert from physics to sociology, White has played an outsized role in the development of social network analysis between 1960 and 2010. His early contributions from the 1960s and 1970s have been hailed as the “Harvard thrust” (Freeman) or the “Harvard breakthrough” (Scott). They center around the notions of vacancy chains and structural equivalence, and the method of blockmodeling analysis. While at Harvard, he shaped two extraordinary generations of graduate students, including Simmel awardees Phillip Bonacich, Ronald Breiger, Kathleen Carley, Bonnie Erickson, Mark Granovetter, Philippa Pattison, and Barry Wellman. His ideas shaped the new economic sociology and the sociology of art. Since 1990, White was the driving force behind “relational sociology” (Emirbayer), offering a theoretical account of social networks and their role in the social world. This approach combines network structuralism with a focus on meaning and culture in networks, as well as on the dynamics of social processes in them. To honor these far-reaching contributions to social network research, Connections invites short pieces of two to five pages. These may center on personal memories, as well as on reflections of White’s impact on personal intellectual trajectories and on developments in the field. The contributions will be selected on the basis of their relevance. Proper articles of up 40 pages will also be considered. Please submit your contributions until October 31, 2024, via the Connections Editorial Manager and make sure to specify “Special Series for Harrison White” in the drop-down menu “Select Article Type”. Connections publishes accepted articles online, open access under Creative Commons CC BY 4.0 license. For any questions, please contact Jan Fuhse (jan.fuhse@...).

When I go to download the program I get File not found (404 error), could you help me with this?