{"id":11125,"date":"2024-08-09T11:18:03","date_gmt":"2024-08-09T11:18:03","guid":{"rendered":"https:\/\/cyber-valley.de\/news\/cyber-valley-research-fund-treibt-exzellenz-in-der-forschung-voran\/"},"modified":"2026-07-15T14:44:13","modified_gmt":"2026-07-15T14:44:13","slug":"cyber-valley-research-fund-treibt-exzellenz-in-der-forschung-voran","status":"publish","type":"news","link":"https:\/\/cyber-valley.de\/de\/news\/cyber-valley-research-fund-treibt-exzellenz-in-der-forschung-voran\/","title":{"rendered":"Cyber\u00a0Valley Research Fund treibt Exzellenz in der Forschung voran"},"content":{"rendered":"\n

Dieser Artikel ist nur auf Englisch verfügbar.<\/p>\n\n

Six of Cyber\u00a0Valley’s founding industrial partners – Amazon, BMW, Bosch, IAV, Porsche, and ZF Friedrichshafen – established the Cyber\u00a0Valley Research Fund to support research projects that combine academic and industrial interests.<\/p><\/strong><\/p>\n\n\n\n

One notable project supported by the Research Fund was Caterina De Bacco\u2019s project \u201cRelaxing Restrictive Independence Assumptions in Networks\u201d. The project aimed to improve our understanding of fundamental patterns in networks through advanced probabilistic generative models. It began in April 2020 and concluded in December 2023.<\/p>\n\n\n\n

By leveraging the growing availability of rich datasets, De Bacco\u2019s project developed advanced network models to capture complex interactions within various domains. The project team created a model that combines node information with network structure to analyze multilayer networks. By developing analytical approximations, the project addressed the complexity of real-world interactions, incorporating features like reciprocity and multiplexity.\n\u00a0\nExtending beyond the concept of conditional independence, the research team developed two new methods for modeling connections between nodes. One uses Poisson distributions and relies on pseudo-likelihood approximations, while the other utilizes Bivariate-Bernoulli distributions. Both methods provide a better understanding of network interactions.\n\u00a0\nAdditionally, the project delved into how the developed ideas and methods could be applied to generalized data sets, specifically hypergraphs, where the interactions involve groups of nodes, and further developed their generative models. These models present several advantages for hypergraph analysis and can effectively identify communities and predict missing connections.\n\u00a0\nThese new models represent a major advancement in network analysis, offering reliable and scalable solutions for understanding complex interactions. The project has set the stage for future research and two more models are currently in development, focusing on multilayer networks with additional information and hierarchical structures.\u00a0\n\u00a0\n\u00a0\nThis project yielded the following publications:<\/p>\n\n\n\n

  1. \n

    M. Contisciani, E. A. Power, and C. De Bacco. \u201cCommunity detection with node attributes in multilayer networks\u201d. Scientific Reports 10.1 (2020), page 15736.<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  2. \n

    K.Higham, M. Contisciani, and C. De Bacco. \u201cMultilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships\u201d. Technological Forecasting and Social Change 179 (2022), page 121628.<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  3. \n

    H. Safdari, M. Contisciani,and C. De Bacco. \u201cGenerative model for reciprocity and community detection in networks\u201d. Physical Review Research 2 (2021).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  4. \n

    M. Contisciani, H. Safdari, and C. De Bacco. \u201cCommunity detection and reciprocity in networks by jointly modelling pairs of edges\u201d. Journal of Complex Networks 10.4 (2022).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  5. \n

    H. Safdari, M. Contisciani, and C. De Bacco. \u201cReciprocity, community detection, and link prediction in dynamic networks\u201d. Journal of Physics: Complexity 3.1 (2022). <\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  6. \n

    C. De Bacco, M. Contisciani, J. Cardoso-Silva, H. Safdari, G. Lima Borges, D. Baptista, T. Sweet, J.-G. Young, J. Koster, C. T. Ross, R. McElreath, D. Redhead, and E. A. Power. \u201cLatent network models to account for noisy, multiply reported social network data\u201d. Journal of the Royal Statistical Society Series A: Statistics in Society 183 (2023).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  7. \n

    H. Safdari, M. Contisciani, and C. De Bacco. \u201cAnomaly, reciprocity, and community detection in networks\u201d. Physical Review Research 5.3 (2023).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  8. \n

    M. Contisciani, F. Battiston, and C. De Bacco. \u201cInference of hyperedges and overlapping communities in hypergraphs\u201d. Nature Communications 13.1 (2022).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  9. \n

    N. Ruggeri, M. Contisciani, F. Battiston, and C. De Bacco. \u201cCommunity detection in large hypergraphs\u201d. Science Advances 28 (2023).<\/span><\/span><\/span><\/span><\/p>\n<\/li>\n

  10. \n

    Q. F. Lotito, M. Contisciani, C. De Bacco, L. Di Gaetano, L. Gallo, A. Montresor, F. Musciotto, N. Ruggeri, and F. Battiston. \u201cHypergraphx: a library for higher-order network analysis\u201d. Journal of Complex Networks 11.3 (2023).<\/span><\/span><\/span><\/span><\/p>\n<\/li><\/ol>\n","protected":false},"featured_media":11472,"parent":0,"template":"","meta":{"_acf_changed":false},"cv-tag":[],"location":[],"network-role":[],"news-category":[],"user-group":[245],"class_list":["post-11125","news","type-news","status-publish","has-post-thumbnail","hentry","user-group-researcher"],"acf":[],"yoast_head":"\nCyber Valley Research Fund treibt Exzellenz in der Forschung voran - 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\/news\/cyber-valley-research-fund-treibt-exzellenz-in-der-forschung-voran\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cyber Valley Research Fund treibt Exzellenz in der Forschung voran - Cyber Valley\" \/>\n<meta property=\"og:description\" content=\"Dieser Artikel ist nur auf Englisch verfügbar. Six of Cyber\u00a0Valley’s founding industrial partners – Amazon, BMW, Bosch, IAV, Porsche, and ZF Friedrichshafen – established the Cyber\u00a0Valley Research Fund to support research projects that combine academic and industrial interests. 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