Local equations for the generalized Lotka-Volterra model on sparse asymmetric graphs
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909012256096256 |
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| author | Machado, David Valigi, Pietro Tonolo, Tommaso Angelini, Maria Chiara |
| author_facet | Machado, David Valigi, Pietro Tonolo, Tommaso Angelini, Maria Chiara |
| contents | Real ecosystems are characterized by sparse and asymmetric interactions, posing a major challenge to theoretical analysis. We introduce a new method to study the generalized Lotka-Volterra model with stochastic dynamics on sparse graphs. By deriving local Fokker-Planck equations and employing a mean-field closure, we can efficiently compute stationary states for both symmetric and asymmetric interactions. We validate our approach by comparing the results with the direct integration of the dynamical equations and by reproducing known results and, for the first time, we map the phase diagram for sparse asymmetric networks. Our framework provides a versatile tool for exploring stability in realistic ecological communities and can be generalized to applications in different contexts, such as economics and evolutionary game theory. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_17499 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Local equations for the generalized Lotka-Volterra model on sparse asymmetric graphs Machado, David Valigi, Pietro Tonolo, Tommaso Angelini, Maria Chiara Populations and Evolution Disordered Systems and Neural Networks Real ecosystems are characterized by sparse and asymmetric interactions, posing a major challenge to theoretical analysis. We introduce a new method to study the generalized Lotka-Volterra model with stochastic dynamics on sparse graphs. By deriving local Fokker-Planck equations and employing a mean-field closure, we can efficiently compute stationary states for both symmetric and asymmetric interactions. We validate our approach by comparing the results with the direct integration of the dynamical equations and by reproducing known results and, for the first time, we map the phase diagram for sparse asymmetric networks. Our framework provides a versatile tool for exploring stability in realistic ecological communities and can be generalized to applications in different contexts, such as economics and evolutionary game theory. |
| title | Local equations for the generalized Lotka-Volterra model on sparse asymmetric graphs |
| topic | Populations and Evolution Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2511.17499 |