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HEДокторантска мрежа2024–2027

BeyondTheEdge · Higher-Order Networks and Dynamics

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

Период
2024-01-01 → 2027-12-31
Финансиране от ЕС
2 553 739 €
Участници
20
Схема
HORIZON-TMA-MSCA-DN

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Накратко на български

Взаимодействията между групи от повече от две единици се анализират чрез математически модели, например как се разпространява болест в групи от хора. Това помага за разбирането и контрола на сложни системи като човешкия мозък или епидемиите.

Този кратък обзор е генериран от изкуствен интелект

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

Резултати накратко

Higher-Order Networks and Dynamics

The scientific aim of the MSCA-DN BeyondTheEdge is to identify the role of nonpairwise ‘higher-order’ interactions in the emergence of complex dynamical behaviour of networks of interacting units. Traditionally, networks are equated with graphs that consist of units together with edges as pairwise relations between two units. However, in network dynamical systems, nonlinear higher order interactions between more than two units often play a critical role in shaping the collective dynamical behaviour of all units: For example, the spread of a disease depends not only on behaviour as pairs of individuals but also how we behave in groups of more than two. Thus, elucidating the role of these higher-order interactions is critical to understand and control the behaviour of complex systems that determine our lives and livelihoods, whether it is the spreading of a lethal disease or the proper functioning of the human brain as a network of billions of interconnected neurons. The main objective of BeyondTheEdge is to identify the role of nonpairwise ‘higher-order’ interactions in network dynamics by crossing the boundaries of the three emergent themes of foundations, structure, data. To summarize, the consortium is addressing the following three research objectives through its three scientific work packages (WP): O1 Cutting across structure and foundations (WP1). We will develop the foundations of a mathematical theory for the network dynamical systems on hypergraphs. Since network dynamical systems with higher-order interactions are a specific class of dynamical systems, we will address the following questions: How can we make best use of existing theory for statements about network dynamical systems, and in particular the role of higher-order interactions represented by a hypergraph? Conversely, can we develop new mathematical theory tailored to this (nongeneric) class of dynamical systems based on structural properties? O2 Cutting across foundations and data (WP2). We will develop data-driven approaches for network dynamics with higher order interactions: Since trajectories of network dynamical systems yield timeseries data, we will address the following questions: What (network) dynamical systems are compatible with given data? Conversely, how can we exploit the (higher-order) network structure to optimally influence/control the dynamics and associated data? O3 Cutting across data and structure (WP3). We will elucidate what structures (graphs, hypergraphs, etc) are appropriate to understand properties of (dynamical) data. Since graphs/hypergraphs are often convenient representations for data, we will address the questions: For given data—such as data from neural recordings—how can we extract higher-order interaction? Conversely, what do these higher-order features capture and in which cases are representations using graphs sufficient?

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

Цел на проекта

Many systems that govern our everyday lives—from communication networks to the human brain—can be seen as networks of interconnected units. Traditionally, networks are equated with graphs where edges give pairwise relations between two units. However, in network dynamical systems, nonlinear higher-order interactions between more than two units often play a critical role in shaping the collective dynamical behaviour of all units: For example, the spread of a disease depends not only on our behaviour as pairs of individuals but also how we behave in groups of more than two. Thus, elucidating the role of these higher-order interactions is critical to understand and control the dynamics of complex systems that determine our lives and livelihoods, whether it is the spreading of a disease or the proper functioning of the human brain as a network of billions of neurons.The doctoral network BeyondTheEdge will identify the role of nonpairwise higher-order interactions in the emergence of complex dynamical behaviour of networks of interacting units. BeyondTheEdge brings together key researchers in an international network that is interdisciplinary (from mathematics to neuroscience) and intersectorial (including academia, private research institutes, and industry) to develop new mathematical insights relevant for real-world problems. BeyondTheEdge will train a cohort of 10 PhD students through research, education, and complementary skills training. This will enable the PhD students to innovate, collaborate, and become leading professionals in academia, industry, or the public sector: Innovative training activities will ensure that all PhD students can apply their skills beyond the academic context and put them in perspective of the wider world. Supervisor training activities ensure that the more junior project partners can shape the PhD education of the future. Thus, BeyondTheEdge will make a lasting contribution that will far outlive the duration of the project.

Оригинален текст от CORDIS (на английски).

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Данни: CORDIS, © Европейски съюз