{"id":510,"date":"2021-02-05T12:27:03","date_gmt":"2021-02-05T11:27:03","guid":{"rendered":"http:\/\/www.networks.unipr.it\/wordpress\/?page_id=510"},"modified":"2026-04-28T11:54:08","modified_gmt":"2026-04-28T09:54:08","slug":"news","status":"publish","type":"page","link":"http:\/\/www.networks.unipr.it\/wordpress\/index.php\/news\/","title":{"rendered":"NEWS"},"content":{"rendered":"

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28 April\u00a0 2026<\/span><\/h2>\n

\"\"The paper \u201cDepth Fragility and Skeletal Universality: Decoupling Topology and Function in Deep Neural Networks,<\/a>\u201d written in collaboration with the Department of Physics of Ho Chi Minh City and Viet Nam National University, has been published in the journal Mathematics.<\/p>\n

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14 January 2026<\/span><\/h2>\n

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Out today in Proceedings of the Royal Society B, our new article uses network science and food web analysis to investigate robustness and keystone species in agricultural ecosystems.<\/a><\/p>\n

Our results show that agricultural food webs managed with genetically modified (GMO\/GMHT) crops are overall less robust than conventionally managed ones, especially in beet fields. When realistic energetic criteria accounting for consumer sensitivity to resource loss are included in the simulations, GMO-managed systems exhibit higher levels of secondary extinctions.<\/p>\n

Omnivory, particularly trophic links between carabid beetles and gastropods, has a strong stabilising effect: agricultural food webs that include these interactions show fewer secondary extinctions. Trophic flexibility helps maintain energy flows and reduces the risk of extinction cascades, highlighting the ecological importance of omnivory in agroecosystems.<\/p>\n

This work is the result of our collaboration with David Bohan (INRAE, Dijon).<\/p>\n

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4 December 2025<\/span><\/h2>\n

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Dr Bellingeri held the seminar \u201cComputational Gastronomy and the Evolution of Food\u201d within the Artificial Intelligence course of the Master\u2019s Degree in Computer Science at the University of Parma.
\nComputational gastronomy brings together mathematics, physics, computer science, food science, and culinary culture to explore how data and algorithms can uncover patterns and connections in the culinary world.<\/p>\n

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4 November 2025<\/span><\/h2>\n

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The paper “Analytics solution for the robustness of incomplete information scale-free networks” has been published on Pyhysics Letters A. Our latest study is part of the long tail of the project \u201cRobustness and dynamics of real social networks with weighted links\u201d, developed in collaboration with the International University (Vietnam) and selected by the Italian Ministry of Foreign Affairs and International Cooperation (MAECI) as a Project of Great Relevance, funded under the Italy\u2013Vietnam Scientific and Technological Cooperation Program 2021\u20132023.
\nIn this research, we analyzed the robustness of scale-free networks when node degree information is incomplete.<\/p>\n

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4 November 2025<\/span><\/h2>\n

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Our CINECA ISCRA project, \u201cFinding bacterial correlations in the human microbiota by leveraging AI technologies\u201d, has been approved.
\nThe project aims to analyse bacterial correlations in human microbiota datasets by combining the power of Leonardo supercomputing and Artificial Intelligence (AI).
\nThrough scalable computational pipelines, BAMAI will build microbial correlation networks capable of capturing highly complex relationships across multiple taxonomic levels.
\nThe ultimate goal is to deepen our understanding of the human microbial ecosystem and support the development of new diagnostic and therapeutic approaches.<\/p>\n

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30 October 2025<\/span><\/h2>\n

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Dr Bellingeri was invited\u00a0 to the final event of the Ecosister project, organized by the University of Modena, to illustrate how complex network science and numerical simulation can be powerful tools for predicting the effects of biodiversity loss in agricultural ecosystems.<\/p>\n

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3 October 2025<\/span><\/h2>\n

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Just published: our new article that bridges network science, graph theory, computation, and data ontology to analyze and compare the structural patterns of traditional cuisine and haute cuisine.<\/p>\n

\ud83d\udd17 https:\/\/lnkd.in\/dCTQsjwx<\/a><\/p>\n

In this study, recipes are modeled as similarity networks: nodes represent individual recipes, and links capture ingredient-based similarities between them. To quantify these relationships, we introduced a novel similarity metric\u2014revealing how traditional and haute cuisines differ in their structural organization.<\/p>\n

\ud83c\udf7d\ufe0f A step forward for computational gastronomy, with exciting applications in AI, personalized recipe generation, and culinary innovation.<\/p>\n

One key insight from the study: the interpretation of data profoundly reshapes the similarity network between recipes. Simple data mining, without critical and informed analysis, risks producing partial or misleading conclusions.<\/p>\n

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26 September\u00a0 2025<\/span><\/h2>\n

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Dr. Michele Bellingeri has presented “Complex Network Science for the Study of Agricultural Ecosystems”<\/a> at the 21st International Conference Applied Statistics 2025<\/a>, September 21\u201323, 2025,\u00a0 Koper\/Capodistria, Slovenia.<\/p>\n

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7 March 2025<\/span><\/h2>\n

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Dr. Michele Bellingeri has been appointed as a Guest Editor of the Research Topic article collection “Exploring Human Interactions through Sociophysics: Dynamics of Opinion Formation”<\/a> of the journal Frontiers in Physics<\/p>\n

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19 February 2025<\/span><\/h2>\n

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The project “Complex Networks analyses through Random Walk Exploration leveraging HPC”, led by Dr. Bellingeri, was funded by an ISCRA call with 80,000 CPU hours for the use of the Leonardo supercomputer.
\nLeonardo is one of the most powerful supercomputers in Europe, located at CINECA, an Italian inter-university consortium specialized in high-performance computing (HPC).
\nThe project will analyze the structure of real large-scale complex networks through the use of high-performance numerical simulations (high-performance computing, HPC)<\/div>\n

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20-24 January 2025<\/span><\/h2>\n

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Dr. Michele Bellingeri participated in the conference \u201c16th Dynamical Systems applied on Biology and Natural Sciences (DSABNS)\u201d held in Naples from 20 to 24 January 2025 with the poster\u00a0 “Keystone Species And Biodiversity Loss In Agricultural Ecological Networks”<\/a><\/div>\n

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25 October 2024<\/span><\/h2>\n

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The Networks Unit, hosted by Dr Davide Salomoni of the ICSC Foundation, visited the Tecnopolo of Bologna, home to important resources for national and international scientific computing:\u00a0 the ICSC (National Research Center in HPC, Big Data and Quantum Computing)<\/a>\u00a0 the Leonardo\u00a0 supercomputer of CINECA<\/a> and new venue of INFN Tier1 computing center.<\/a><\/div>\n

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21 October 2024<\/span><\/h2>\n

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The article entitled \u2018Machine Learning Monte Carlo Approaches and Statistical Physics Notions to Characterize Bacterial Species in Human Microbiota<\/a>\u2019 is recently published in the international journal Machine Learning and Knowledge Extraction.<\/p>\n

The research is the result of the collaboration between the Networks Unit and the Probiogenomics Laboratory of the University of Parma, directed by Prof. Marco Ventura, with the contribution of the Computational Microbiology Unit coordinated by Prof. Christian Milani ( http:\/\/probiogenomics.unipr.it\/pbi\/<\/a>\u00a0 ).
\nThis study introduces a new approach that combines statistical physics with Monte Carlo (MC) methods to characterize bacterial species in the human microbiota at different biological ages.
\nOur results show that the composition of the microbiota experiences a significant transition from early childhood to adulthood.
\nThe study shows how low-prevalence species can still play a significant role in characterizing age groups.
\nFinally, the research proposes a multicriteria classification of species in different age groups by integrating statistical, physical and simulation methods.<\/p>\n

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\"\"26 July 2024<\/span><\/h2>\n

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Dr. Michele Bellingeri has presented “Complex Networks of Ingredients and Receipes” at\u00a0 the international summer course in scientific gastronomy\u00a0 held from July 22-26, 2024, in the Cirfood District, Reggio Emilia, Italia.<\/p>\n

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\"\"27 June 2024<\/span><\/h2>\n

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The Networks Unit participated\u00a0 in\u00a0 the Conference “Ecosister in Parma: The Spoke 4 & Spoke 6”,\u00a0 27 June 2024, Sala Congressi delle Scienze \u2013 Campus, Parma, \u00a0with the poster “Forecasting biodiversity loss in agricultural ecological networks”<\/a><\/p>\n

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\"\"15 June 2024<\/span><\/h2>\n

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The Networks Unit participated in the “WeMakeFuture”<\/a> event held at the Bologna Fiere from 13 to 15 June 2024, with the poster “AI for Biodiversity”.<\/p>\n

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\"\"17 May 2024<\/span><\/h2>\n

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The paper “Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal”<\/a>, has been published on Mathematics. This work was carried out in collaboration with the Network Science Research Group of the Indian Institute of Information Technology in Kottayam.<\/p>\n

\"\"16 April 2024<\/span><\/h2>\n

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The Network Unit participated in the poster session of the Computer Science Day 2024<\/a> held at the Mathematics building of the University of Parma.<\/p>\n

\"\"8 March 2024<\/span><\/h2>\n

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The paper “The Critical Role of Networks to Describe Disease Spreading Dynamics in Social Systems: A Perspective<\/a>” has been published on Mathematics.\u00a0 This review underscores the critical significance of incorporating networks science in epidemiology. Classic mathematical compartmental models (CMs) employed to describe epidemic spreading may fail to capture the intricacies of real disease dynamics.<\/p>\n

\"\"11 December 2023<\/span><\/h2>\n

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Professors Cassi, Alfieri and Bellingeri, of the Network Unit of the University of Parma participated in a mission from 4 to 9 December 2023 at the laboratory of Prof. Quang Nguyen of the International University of Ho Chi Minh City<\/a>. The mission is part of the project\u00a0 ‘Robustness and important components of real-world social weighted networks’ in the field of ICT funded by the MAECI.<\/p>\n

\"\"30 November 2023<\/span><\/h2>\n

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The paper “Random Walks-Based Node Centralities to Attack Complex Networks” has been published on Mathematics.\u00a0 In this manuscript, we propose four new measures of node centrality based on Random Walks (RW) and we compare the efficacy of the new RW node centralities for network dismantling with effective node removal strategies.<\/p>\n

\"\"11 October 2023<\/span><\/h2>\n

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The paper “Forecasting real-world complex networks\u2019 robustness to node attack using network structure indexes<\/a>” has been published on Frontiers in Physics. In this study, we simulate the degree and betweenness node attack over a large set of 200 real-world networks from different areas of science.<\/p>\n

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29 September 2023<\/span><\/strong><\/p>\n

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On September 29th\u00a0 Dr. Michele Bellengeri presented\u00a0\u201cResilience and biodiversity loss of agricultural food webs\u201d<\/a>\u00a0in the\u00a0 \u00a0ECOSISTER Spoke6 Scientific&techincal progress workshop.<\/a><\/p>\n

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11 August 2023<\/span><\/h2>\n

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The first paper resulting from the collaboration between our research group and the Network Science Research Group<\/a> of the Indian Institute of Information Technology in Kottayam has been published.<\/p>\n

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22 June 2023<\/span><\/h2>\n

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The Networks Unit participated at the III Conference of the Italian Society of Statistical Physics – SIFS<\/a> , which was held in Parma from 21 to 23 June 2023, with the poster “Robustness and diffusion of properties in real networks”.<\/p>\n

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26 May 2023<\/span><\/h2>\n

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Dr. Michele Bellingeri has been appointed as a Guest Editor of the Research Topic article collection “Compartmental Models for Social Interactions”<\/a> of the journal Frontiers in Physics – section Social Physics.<\/p>\n

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28 March 2023<\/span><\/h2>\n

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The paper “Considering weights in real social networks: A review”<\/a> has been published on Frontiers in Physics. In this work we outline how weighted social networks (WSN) methodology may improve the modeling of several real problems in social sciences.<\/p>\n

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20 February 2023<\/span><\/h2>\n

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Our network\u00a0 “Plant Photosystem I” (PSI) excitation energy transfer network\u201d,<\/a> has been published in the Netzschleuder repository.<\/p>\n

The nodes represent chromophores with different identities, while edges represent FRET transfer between chromophores. The link directionality connecting nodes in the PSI network depends on the energy levels of the connected chromophores. The weights correspond to the FRET Efficiency between connected chromophores.<\/p>\n

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26 January 2023<\/span><\/h2>\n

\"\"The paper “Effective node vaccination and containment strategies to stop the spread of the SIR epidemic in face-to-face contact networks in the real world”\u00a0has been presented at the International Conference on Computing and Communication Technologies (RIVF) held in Ho Chi Minh City, Vietnam, from 20 to 22 December 2022 and published in the conference proceedings ( doi: 10.1109\/RIVF55975.2022.10013812<\/a>).
\nThe effectiveness of the use of face mask and vaccination policies to curb the spread of the COVID-19 epidemic was evaluated by SIR simulations in four real face-to-face contact networks.\u00a0 This work has been realized within the “Progetto di Grande Rilevanza”<\/a> collaboration,\u00a0 funded by MAECI.<\/p>\n

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14 December 2022<\/span><\/h2>\n

\"\"A new study by the Networks Unit entitled “A comparison of node vaccination strategies to halt SIR epidemic spreading in real-world complex networks” has been published in the journal Scientific Reports.<\/p>\n

In this study we analyze the effectiveness of different vaccination strategies to curb the epidemic spread in social networks.<\/p>\n

The results of this study are the result of our unit’s effort to setup useful tools to slow down or stop epidemics, such as the recent case of Covid 19.<\/p>\n

The research was carried out in international collaboration with the John von Neumann Institute of Ho Chi Minh (Vietnam), and the University of Duy Tan located in the city of Da Nang (Vietnam), within the project ‘Robustness and key components of social networks with weighted links’ financed with the contribution of the Ministry of Foreign Affairs and International Cooperation (MAECI) for the “Progetti di grande rilevanza Italia-Vietnam” in the field of ‘Information and Communication Technologies.<\/p>\n

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3 November 2022<\/span><\/h2>\n

\"\"The Parma Networks Unit has published the results of an extensive research on the structural factors of complex networks capable of influencing the speed of epidemic spread in real networks.<\/p>\n

The article entitled ‘Network structure indexes to forecast epidemic spreading in real-world complex networks’ was published in the international journal Frontiers in Physics, in the special issue ‘Epidemic Models on Networks’ dedicated to the study of epidemic spread on real networks.<\/p>\n

This article investigates which structural indicators of complex networks are most important to consider in predicting the rate of epidemic spread in real networks.
\nThe epidemic spread was modeled through a SIR-like dynamics and the salient features of the network were described by 40 different structural indicators. The structural indicators considered the number of links in the network, the distance between nodes, the presence of community of nodes, the difference in connectivity between nodes, and many other characteristics of fundamental importance in the science of networks.<\/p>\n

Research shows that the speed of epidemic spread is mainly determined by the ‘average distance’ between nodes from the network, that is, by the average number of bonds that must be crossed to travel between pairs of nodes. In addition to this, the density of links in the network was found to be a factor of poor predictive power of the speed of epidemic spread.<\/p>\n

These results indicate that to predict the rate of epidemic spread in real complex networks it is not very effective to consider the simple number of links present between the nodes of the network, but it is instead necessary to consider the structure of the network and how it affects the distance between nodes.<\/p>\n

This is particularly important in the context of measures taken to slow down or stop epidemics in social networks, such as the recent case of Covid 19.
\nThe research was carried out in international collaboration with the John von Neumann Institute in Ho Chi Minh (Vietnam), and the University of Duy Tan located in the city of Da Nang (Vietnam), as part of the ‘Robustness and key components of social networks with weighted ties ‘funded with the contribution of the Ministry of Foreign Affairs and International Cooperation (MAECI) for projects of Great Relevance Italy-Vietnam in the field of’ Information and Communication Technologies.<\/p>\n

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31 December 2021<\/span><\/h2>\n

\"\"The first year of activity of our project ‘Robustness and dynamics of real social networks with weighted links’ is completed today. The project, developed in collaboration with Ton Duc Thang University (Vietnam), has been selected by the Ministry of Foreign Affairs and International Cooperation (MAECI) as a Project of Great Relevance and funded under the ‘Italy-Vietnam Scientific and Technological Cooperation Program for the period 2021-2023\u2032.
\nThe work produced three publications in international scientific journals over the course of 2021.<\/p>\n

Publications:<\/span><\/p>\n