[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125228-en":3,"doc-seo-125228-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125228,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains","Networks provide a foundational framework for modeling complex systems across biological interactions, social structures, economic markets, and information networks. Machine learning improves pattern extraction and predictive performance in graph-based applications such as market design, recommendation, and biomedical discovery. Key challenges remain, especially overreliance on simplified models and synthetic benchmarks that ignore structured dependencies and temporal, structured missingness. This dissertation develops fairness-aware approaches for stable matching with correlated preferences, a principled model selection method for signed networks using GRASMOS, and improved link-prediction evaluation with temporal evolution to reduce generalization gaps.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n3-2025  \nMachine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains  \nAngelina Brilliantova[lb9849@rit.edu](lb9849@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nBrilliantova, Angelina, \"Machine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Dissertation is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nMachine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains  \nby  \nAngelina Brilliantova  \nA dissertation submitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nin Computing and Information Sciences  \nB. Thomas Golisano College of Computing and  \nInformation Sciences  \nRochester Institute of Technology  \nRochester, New York  \nMarch, 2025  \nMachine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains  \nby  \nAngelina Brilliantova  \nCommittee Approval:  \nWe, the undersigned committee members, certify that we have advised and/or supervised the candidate on the work described in this dissertation. We further certify that we have reviewed the dissertation manuscript and approve it in partial fulfillment of the requirements of the degree of Doctor of Philosophy in Computing and Information Sciences.  \n\n| Dr. Ivona Bez´akov´a\u003Cbr>Dissertation Advisor | Date |\n| --- | --- |\n| Dr. Stanisław Radziszowski\u003Cbr>Dissertation Committee Member | Date |\n| Dr. Varsha Dani\u003Cbr>Dissertation Committee Member | Date |\n| Dr. Rui Li\u003Cbr>Dissertation Committee Member | Date |\n| Dr. Anurag Agarwal\u003Cbr>Dissertation Defense Chairperson\u003Cbr>Certified by: | Date |\n\nDr. Pengcheng Shi Date  \nPh.D. Program Director, Computing and Information Sciences  \niii  \n©2025 Angelina Brilliantova All rights reserved.  \nMachine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains  \nby  \nAngelina Brilliantova  \nSubmitted to the  \nB. Thomas Golisano College of Computing and Information Sciences Ph.D. Program in  \nComputing and Information Sciences  \nin partial fulfillment of the requirements for the  \nDoctor of Philosophy Degree  \nat the Rochester Institute of Technology  \nAbstract  \nNetworks serve as a fundamental framework for modeling complex systems across diverse domains, from biological interactions and social structures to economic markets and information networks. Machine learning has significantly enhanced the ability to extract meaningful patterns from graph-based systems, improving predictive accuracy in applications such as market design, recommendation systems, and biomedical discovery. Despite the successes of machine learning in analyzing complex networks, several fundamental challenges remain. A major issue is the reliance on simplistic models for theoretical analysis, simulation, and benchmarking, which often fail to capture the complexities of real-world networked systems. Many theoretical studies focus on oversimplified generative models that assume uniform randomness in edge formation, preference rankings, or network evolution, limiting their applicability to real-world problems. While these models provide analytical tractability, they often overlook the structured dependencies and correlations that shape real-world interactions.  \nSimilarly, the simulation and benchmarking of machine learning models for networks frequently rely on synthetic datasets that do n","cbCaimwwkhnnDjVh","https://ap.wps.com/l/cbCaimwwkhnnDjVh","pdf",2988394,1,132,"English","en",105,"# Abstract\n## Fairness in stable matching with correlated preferences\n## Model selection for signed networks and GRASMOS\n## Temporal evaluation for link prediction","[{\"question\":\"What problem does the dissertation address in machine learning for complex networks?\",\"answer\":\"It targets limitations of simplified theoretical models and synthetic benchmarks that fail to capture structured dependencies and temporal, structured missingness in real-world networks.\"},{\"question\":\"How does the work handle fairness in stable matching?\",\"answer\":\"It studies fairness under correlated preferences, analyzes how preference asymmetries affect fairness outcomes, and proposes efficient fairness-aware solutions that preserve stability.\"},{\"question\":\"What contributions are made to model selection and evaluation for graph tasks?\",\"answer\":\"It introduces GRASMOS for maximum-likelihood model selection in signed networks to infer realistic sign assignments, and it improves link-prediction evaluation by incorporating temporal graph evolution to reduce generalization gaps.\"}]","Machine Learning for Complex Networks: Generalization, Fairness, and Model Selection in Graph-Based Systems Across Social and Biological Domains | 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