RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks

[2608.14504] RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Social and Information Networks arXiv:2608.14504 (cs) [Submitted on 14 Aug 2026] Title: RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks Authors: Emily J Evans , Weihong Guo , Carlotta Domenicon View a PDF of the paper titled RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks, by Emily J Evans and 2 other authors View PDF HTML (experimental) Abstract: This paper introduces a dynamic role discovery technique in temporal dynamic networks, utilizing temporally regularized Non-negative Matrix Factorization (NMF). Our technique differs from existing dynamic role analysis techniques by creating a consistent set of roles across all time periods, as well as a universal transition matrix that describes the probability of transitioning between roles. We also apply a regularization penalty to ensure that role membership does not change dramatically between time periods making our model more robust against real-world noise. We test our data on five real-world and one synthetically simulated dataset using both engineered and automatically generated features. We demonstrate that the proposed regularized role detection method, for appropriate regularization weight parameter reduces prediction errors compared to other techniques. Furthermore, trace analysis of the transition matrices indicates that our method yields a more stable system, that is, individuals are more likely to stay in their roles with fewer arbitrary transitions. Our model learns time-aligned roles, captures behavioral transitions over time, and scales efficiently to large and sparse graphs. Subjects: Social and Information Networks (cs.SI) Cite as: arXiv:2608.14504 [cs.SI] (or arXiv:2608.14504v1 [cs.SI] for this version) https://doi.org/10.48550/arXiv.2608.14504 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Emily Evans [ view email ] [v1] Fri, 14 Aug 2026 17:17:34 UTC (658 KB) Full-text links: Access Paper: View a PDF of the paper titled RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks, by Emily J Evans and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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