Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913100098174976 |
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| author | Marcús, Aina Ferrà Jankowski, Robert Miñana, Meritxell Vila Casacuberta, Carles Serrano, M. Ángeles |
| author_facet | Marcús, Aina Ferrà Jankowski, Robert Miñana, Meritxell Vila Casacuberta, Carles Serrano, M. Ángeles |
| contents | Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometry. Revealing the dimensionality of this latent space can disentangle the structural complexity of communities, impact efficient network navigation, and fundamentally shape connectivity and system behavior. We introduce a topological data analysis weighting scheme for graphs based on chordless cycles to estimate network dimensionality in a data-driven way. We further show that the resulting descriptors can effectively estimate network dimensionality using a neural network architecture trained on a synthetic graph database constructed for this purpose, which requires no retraining to transfer effectively to real-world networks. Thus, by combining cycle-aware filtrations, algebraic topology, and machine learning, our approach provides a robust and effective method for uncovering the hidden geometry of complex networks and guiding accurate modeling and low-dimensional embedding. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08350 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis Marcús, Aina Ferrà Jankowski, Robert Miñana, Meritxell Vila Casacuberta, Carles Serrano, M. Ángeles Physics and Society Machine Learning Algebraic Topology 55N31 Many complex networks, ranging from social to biological systems, exhibit structural patterns consistent with an underlying hyperbolic geometry. Revealing the dimensionality of this latent space can disentangle the structural complexity of communities, impact efficient network navigation, and fundamentally shape connectivity and system behavior. We introduce a topological data analysis weighting scheme for graphs based on chordless cycles to estimate network dimensionality in a data-driven way. We further show that the resulting descriptors can effectively estimate network dimensionality using a neural network architecture trained on a synthetic graph database constructed for this purpose, which requires no retraining to transfer effectively to real-world networks. Thus, by combining cycle-aware filtrations, algebraic topology, and machine learning, our approach provides a robust and effective method for uncovering the hidden geometry of complex networks and guiding accurate modeling and low-dimensional embedding. |
| title | Chordless cycle filtrations for dimensionality detection in complex networks via topological data analysis |
| topic | Physics and Society Machine Learning Algebraic Topology 55N31 |
| url | https://arxiv.org/abs/2509.08350 |