HEИндивидуална стипендия2023–2025

DynaNet · Mathematical random graph models for real-world dynamical networks

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2023-09-01 → 2025-08-31
Финансиране от ЕС: 173 847 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

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Накратко на български

Математическите модели на динамични мрежи изследват как се променят връзките в системи като мозъка или интернет, когато се добавят или премахват елементи. Това помага за по-доброто разбиране на структурата и поведението на реални процеси, като разпространението на вируси или фалшиви новини.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Mathematical random graph models for real-world dynamical networks

Networks are everywhere around us and play an important role in our lives. Think of the neurons in a brain, viruses like COVID-19 spreading through the population (a social network), fake news on social media networks like Instagram influencing elections, or computer viruses attacking digital infrastructure via computer networks. Mathematics can contribute to the understanding of such real-world networks, in terms of their structure and and behaviour, by analysing mathematical models of such networks. Due to the abstract level of mathematics, such analyses are widely applicable, e.g. computer viruses spreading through computer networks and viruses spreading through the population. Over the last two decades, evolving random graph models have proved to be one of the most successful and popular models for explaining the emergence of empirically observed features in many real-world networks, such as well-connectedness (e.g. every two Facebook users are separated by a chain of about 4 to 5 befriended users), and the existence of few extremely well-connected nodes called hubs in networks (e.g. millions of webpages link towards Google). So far, only evolving models incorporating the addition of vertices and edges to the graph have been studied in detail. Such a dynamical construction, however, is far from realistic: Real-world networks allow for both addition as well as deletion of vertices and edges. Examples include social media networks (users create/delete accounts and connections); biological networks like the brain (neural plasticity, the brain can grow/shrink); and the Internet (devices can break down and be removed from the network). Very little attention, however, has been dedicated to the study of mathematical models that incorporate such realistic dynamics. The overall objectives of this project are to address these shortcomings, by: (1) Studying the existence of hubs in dynamical networks that incorporate dynamics of both addition and removal, (2) Studying the interconnectedness of dynamical networks that incorporate dynamics of both addition and removal, (3) Studying the spread of information on networks over time.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Networks are all around us and hugely affect us: the neurons in our brain, COVID-19 spreading through the population, or computer viruses attacking digital infrastructure via computer networks. Experts estimate that in the US alone, their government faced costs over 13.7 billion dollars due to cyberattacks in 2018. Insight into the structure and vulnerability of computer networks can help strengthen our defences against cyberattacks, reducing future cost and disruption. Analysing mathematical models of real-world networks can provide such insight. The abstract level of mathematics also makes such analyses widely applicable in various settings like hacking of computer networks and viral pandemics. Most networks change over time. They grow, shrink, and gain and lose connections, like neural plasticity, friendships made and lost, and computers breaking down. Many mathematical models, however, do not incorporate such realistic dynamics of evolving real-world networks. They only allow for network growth, not for removal of nodes and connections. This creates a gap between the theoretical knowledge and the practical use thereof.I aim to close this gap by analysing properties of two models for real-world networks that incorporate realistic dynamics: Preferential attachment with vertex and edge removal and first-passage percolation on weight-dependent random connection models. The outcomes of this research can influence policy discussions around protecting digital infrastructure and our response to viral pandemics, tying in with the European Commission’s current priorities regarding digitalisation and health within NextGenerationEU. The research through training provided within this fellowship will provide me with leadership skills and more experience in writing grant proposals and supervising students. Combined with its scientific training, it will help me solidify my potential as an early-career researcher and obtain a tenure-track position at a top European university.

Оригинален текст от CORDIS (на английски).

Участници

UNIVERSITAET AUGSBURG · AugsburgКоординаторГермания