Higher-order Connection Laplacians for Directed Simplicial Complexes
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914966122004480 |
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| author | Gong, Xue Higham, Desmond J. Zygalakis, Konstantinos Bianconi, Ginestra |
| author_facet | Gong, Xue Higham, Desmond J. Zygalakis, Konstantinos Bianconi, Ginestra |
| contents | Higher-order networks encode the many-body interactions existing in complex systems, such as the brain, protein complexes, and social interactions. Simplicial complexes are higher-order networks that allow a comprehensive investigation of the interplay between topology and dynamics. However, simplicial complexes have the limitation that they only capture undirected higher-order interactions while in real-world scenarios, often there is a need to introduce the direction of simplices, extending the popular notion of direction of edges. On graphs and networks the Magnetic Laplacian, a special case of Connection Laplacian, is becoming a popular operator to treat edge directionality. Here we tackle the challenge of treating directional simplicial complexes by formulating Higher-order Connection Laplacians taking into account the configurations induced by the simplices' directions. Specifically, we define all the Connection Laplacians of directed simplicial complexes of dimension two and we discuss the induced higher-order diffusion dynamics by considering instructive synthetic examples of simplicial complexes. The proposed higher-order diffusion processes can be adopted in real scenarios when we want to consider higher-order diffusion displaying non-trivial frustration effects due to conflicting directionalities of the incident simplices. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_07631 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Higher-order Connection Laplacians for Directed Simplicial Complexes Gong, Xue Higham, Desmond J. Zygalakis, Konstantinos Bianconi, Ginestra Social and Information Networks Disordered Systems and Neural Networks Statistical Mechanics Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability Higher-order networks encode the many-body interactions existing in complex systems, such as the brain, protein complexes, and social interactions. Simplicial complexes are higher-order networks that allow a comprehensive investigation of the interplay between topology and dynamics. However, simplicial complexes have the limitation that they only capture undirected higher-order interactions while in real-world scenarios, often there is a need to introduce the direction of simplices, extending the popular notion of direction of edges. On graphs and networks the Magnetic Laplacian, a special case of Connection Laplacian, is becoming a popular operator to treat edge directionality. Here we tackle the challenge of treating directional simplicial complexes by formulating Higher-order Connection Laplacians taking into account the configurations induced by the simplices' directions. Specifically, we define all the Connection Laplacians of directed simplicial complexes of dimension two and we discuss the induced higher-order diffusion dynamics by considering instructive synthetic examples of simplicial complexes. The proposed higher-order diffusion processes can be adopted in real scenarios when we want to consider higher-order diffusion displaying non-trivial frustration effects due to conflicting directionalities of the incident simplices. |
| title | Higher-order Connection Laplacians for Directed Simplicial Complexes |
| topic | Social and Information Networks Disordered Systems and Neural Networks Statistical Mechanics Adaptation and Self-Organizing Systems Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.07631 |