{"id":8366,"date":"2026-03-13T10:25:00","date_gmt":"2026-03-13T10:25:00","guid":{"rendered":"https:\/\/cyber-valley.de\/?post_type=researchfund-project&#038;p=8366"},"modified":"2026-09-01T11:59:20","modified_gmt":"2026-09-01T11:59:20","slug":"anomaly-detection-in-networks-reciprocated-and-dynamic-networks","status":"publish","type":"researchfund-project","link":"https:\/\/cyber-valley.de\/de\/researchfund-project\/anomaly-detection-in-networks-reciprocated-and-dynamic-networks\/","title":{"rendered":"Anomaly Detection in Networks: Reciprocated and Dynamic Networks"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Overview<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Led by Caterina De Bacco, Hadiseh Safdari, and Martina Contisciani, this project investigated how to detect unusual patterns in complex networks. The project was supported by the Cyber\u00a0Valley Research Fund from September 2021 until December 2023.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The researchers developed probabilistic models that combine anomaly detection with community detection. In this context, communities are groups of nodes\u2014such as users, organizations, or transport hubs\u2014that interact more frequently with one another. Interactions that do not fit these regular patterns can then be identified as potential anomalies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The project examined static, reciprocal, and dynamic networks, in which connections appear and disappear over time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Key results<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Developed models that infer both hidden community structures and the probability that an edge is anomalous.<\/li>\n\n\n\n<li>Showed that anomalies can be detected using network structure alone, without requiring additional information about individual nodes.<\/li>\n\n\n\n<li>Extended the model to account for reciprocity, meaning that connections in opposite directions\u2014such as mutual communication\u2014can influence what counts as regular behavior.<\/li>\n\n\n\n<li>Developed DynACD, a model for dynamic networks that uses temporal information to identify unusual changes in interactions.<\/li>\n\n\n\n<li>Demonstrated that the dynamic model performed better than several comparison methods in detecting anomalous edges, particularly when the timing of interactions was important.<\/li>\n\n\n\n<li>Showed that identifying and removing anomalous edges can improve community detection and that the model can also identify unexpected or potentially missing connections.<\/li>\n\n\n\n<li>Released open-source software implementations, including ACD and CRAD.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">This project has yielded three peer reviewed publications:&nbsp;<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>\u201cAnomaly detection and community detection in networks\u201d, H. Safdari and C. De Bacco, J Big Data 9, 122,&nbsp;<a rel=\"noopener\" target=\"_blank\" href=\"https:\/\/doi.org\/10.1186\/s40537-022-00669-1\">https:\/\/doi.org\/10.1186\/s40537-022-00669-1<\/a>, 2022.<\/li>\n\n\n\n<li>\u201cAnomaly, reciprocity, and community detection in networks\u201d, H. Safdari, M. Contisciani and C. De Bacco, Phys. Rev. Research 5, 033084,&nbsp;<a rel=\"noopener\" target=\"_blank\" href=\"https:\/\/link.aps.org\/doi\/10.1103\/PhysRevResearch.5.033084\">https:\/\/link.aps.org\/doi\/10.1103\/PhysRevResearch.5.033084<\/a>, 2023.<\/li>\n\n\n\n<li>\u201cCommunity detection and anomaly prediction in dynamic networks\u201d, H. Safdari and C. De Bacco, Communications Physic<em>s<\/em>, 7, 397. <a href=\"https:\/\/doi.org\/10.1038\/s42005-024-01889-y\" target=\"_blank\" rel=\"noreferrer noopener\">doi:10.1038\/s42005-024-01889-y<\/a>, 2024.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">It also yielded two inventions:&nbsp;<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a rel=\"noopener\" target=\"_blank\" href=\"https:\/\/github.com\/hds-safdari\/Anomaly_Community_Detection\">https:\/\/github.com\/hds-safdari\/Anomaly_Community_Detection<\/a><\/li>\n\n\n\n<li><a rel=\"noopener\" target=\"_blank\" href=\"https:\/\/github.com\/hds-safdari\/CRAD-algorithm\">https:\/\/github.com\/hds-safdari\/CRAD-algorithm<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"featured_media":0,"parent":0,"template":"","meta":{"_acf_changed":true},"cv-tag":[],"user-group":[],"class_list":["post-8366","researchfund-project","type-researchfund-project","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Anomaly Detection in Networks: Reciprocated and Dynamic Networks - Cyber Valley<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cyber-valley.de\/de\/researchfund-project\/anomaly-detection-in-networks-reciprocated-and-dynamic-networks\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Anomaly Detection in Networks: Reciprocated and Dynamic Networks - Cyber Valley\" \/>\n<meta property=\"og:description\" content=\"Overview Led by Caterina De Bacco, Hadiseh Safdari, and Martina Contisciani, this project investigated how to detect unusual patterns in complex networks. The project was supported by the Cyber\u00a0Valley Research Fund from September 2021 until December 2023. The researchers developed probabilistic models that combine anomaly detection with community detection. 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