From tieliny at andrew.cmu.edu Thu Jan 25 19:01:33 2024 From: tieliny at andrew.cmu.edu (Katy Yu) Date: Mon Mar 25 10:47:53 2024 Subject: [statnet_help] Error in ergm_proposal.NULL when setting up bridge sampling Message-ID: Dear Statnet Community, I am reaching out for assistance regarding an issue we encountered in our research on Cross-National Collaboration in Open-Source Software Development. Our study investigates cultural and religious divisions in online social interactions, inspired by Huntington's theory of post-Cold War divisions structured by culture and religion. Our analysis utilizes ERGM to quantify country-level homophily within eight "civilizations" as classified by Huntington. The network data comprises a directed network with each node representing a country. The edges denote cross-country collaborations on GitHub, encompassing 91 nodes and 425 edges. While the basic ERGM model "edges + nodematch('civilizations', diff = TRUE)" provides statistics successfully, we encounter an error when integrating additional methods such as 'mutual'. This error occurs even after the model converges. [image: Screenshot 2024-01-25 at 12.19.28 PM.png] We have attempted various constraints and explored online resources to resolve this issue but to no avail. Could you kindly provide guidance or suggest potential solutions to address this error? Thank you for your support and expertise. Kind regards, Katy -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Screenshot 2024-01-25 at 12.19.28 PM.png Type: image/png Size: 211452 bytes Desc: not available URL: From statnet_help at u.washington.edu Thu Apr 4 01:41:20 2024 From: statnet_help at u.washington.edu (Gilad Ravid via statnet_help) Date: Thu Apr 4 01:41:25 2024 Subject: [statnet_help] ergm levels selection Message-ID: Greetings, I need to build a model where I can control the baseline and the exclude list of levels in nodefactor term. For example, I want to specify that the baseline will be the X level and exclude levels Y and Z due to empty examples in the network (to avoid the Inf coefficient). Many thanks, Gilad -------------- next part -------------- An HTML attachment was scrubbed... URL: From statnet_help at u.washington.edu Wed Apr 17 15:32:19 2024 From: statnet_help at u.washington.edu (MINGHUA ZHANG via statnet_help) Date: Wed Apr 17 15:32:25 2024 Subject: [statnet_help] ergm algorithm from scratch Message-ID: Hi Statnet, My name is Minghua, and I am an undergrad at the University of Wisconsin, Madison. I am using ERGM in my research to model a healthcare referral network that is bipartite (with one set of nodes has outdegree strictly equal to 1 for every node) and contains missing edges. After reading some literatures and exploring the Statnet packages, I believe that Statnet is unable to handle this scenario. Thus, I tried to code up fitting algorithms that can handle this special case. Currently, I am trying to just have a model that can fit the most general graph (undirected, unipartite, no missing info) with one graph feature, number of edges. After comparing with Statnet's result, I believe my theta update algorithm might not be accurate. My current theta updating method is from the book ERGM for Social Network by Lusher, Koskinen, and Robins using Raphson newton method. Details of the exact formulas are attached in the pdf. I am wondering if you can point me to the literatures where I can find other updating algorithms, or codes where I can check out how statnet fit the model. I am greatly appreciative of any help you can provide! Best, Minghua -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: Referrals.pdf Type: application/pdf Size: 67227 bytes Desc: Referrals.pdf URL: From statnet_help at u.washington.edu Sun Apr 28 17:23:08 2024 From: statnet_help at u.washington.edu (Abir Khazaal via statnet_help) Date: Sun Apr 28 17:23:17 2024 Subject: [statnet_help] Detecting communities Message-ID: Hi there, I really hope someone can assist me. Isn?t there a package within the statnet project that I can use for detecting communities within temporal networks? Thanks, Abir (Abby) Khazaal | PhD candidate Lab Manager | Project Coordinator Biomedical AI Laboratory | (Vafaee Lab) School of Biotechnology and Biomolecular Sciences | (BABS) Level 2, E26 | UNSW SYDNEY NSW 2052 AUSTRALIA If I send you an email beyond your regular work hours, please understand that I do not anticipate you to review, reply, or act on it outside of your typical work schedule. [A picture containing logo Description automatically generated] [A logo with a white background Description automatically generated] -------------- next part -------------- An HTML attachment was scrubbed... URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image001.png Type: image/png Size: 50943 bytes Desc: image001.png URL: -------------- next part -------------- A non-text attachment was scrubbed... Name: image002.png Type: image/png Size: 73776 bytes Desc: image002.png URL: From statnet_help at u.washington.edu Mon Apr 29 05:07:25 2024 From: statnet_help at u.washington.edu (James Moody via statnet_help) Date: Mon Apr 29 05:07:34 2024 Subject: [statnet_help] Detecting communities In-Reply-To: References: Message-ID: Hi Abir - How dynamic is your data? i.e. are you looking at real-time sorts of dynamics (fine grained time) or waves of data collected (course grained time)? While there are likely some avenues you could explore using latent communities within the Statnet ecosystem; I think most of the community detection tools are pre-modeling steps you'd take using other tools. Two reasonable approaches if your data is reasonably course grained: 1. Cluster each wave separately. This is the simplest-non-stupid thing to do. Perfectly defensible as your optimizing within the wave and allows nodes maximal opportunity for change across waves. Cost is that you have multiple communities completely independent across T, so (a) you'll have to do the work to figure out if a community at time t is the same as at t+1 (and it will never be exactly the same), and (b) there will be noise. 1. Convert your data to a multi-layer network, where each node identity is linked to itself across waves (*i.e. stack the edgelists, and add i_t --> i_t+1 to the edgelist). You'll have to do some data munging to get the IDs all sorted and such. Then cluster the multi-layer network. You can (should) adjust weights for clustering within/between layers - which is something of an art. Peter Mucha's team has done a fair amount of work on this. See https://arxiv.org/abs/0911.1824 to get you started.... PTs Jim James Moody Professor of Sociology Director, Duke Network Analysis Center From: statnet_help On Behalf Of Abir Khazaal via statnet_help Sent: Sunday, April 28, 2024 8:23 PM To: statnet_help@u.washington.edu Subject: [statnet_help] Detecting communities Hi there, I really hope someone can assist me. Isn't there a package within the statnet project that I can use for detecting communities within temporal networks? Thanks, Abir (Abby) Khazaal | PhD candidate Lab Manager | Project Coordinator Biomedical AI Laboratory | (Vafaee Lab) School of Biotechnology and Biomolecular Sciences | (BABS) Level 2, E26 | UNSW SYDNEY NSW 2052 AUSTRALIA If I send you an email beyond your regular work hours, please understand that I do not anticipate you to review, reply, or act on it outside of your typical work schedule. 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