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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.04884 |
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| _version_ | 1866909827054174208 |
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| author | Schroeder, Adam Funk, Russell Guan, Jingyi Okonek, Taylor Ziegelmeier, Lori |
| author_facet | Schroeder, Adam Funk, Russell Guan, Jingyi Okonek, Taylor Ziegelmeier, Lori |
| contents | Thresholding--the pruning of nodes or edges based on their properties or weights--is an essential preprocessing tool for extracting interpretable structure from complex network data, yet existing methods face several key limitations. Threshold selection often relies on heuristic methods or trial and error due to large parameter spaces and unclear optimization criteria, leading to sensitivity where small parameter variations produce significant changes in network structure. Moreover, most approaches focus on pairwise relationships between nodes, overlooking critical higher-order interactions involving three or more nodes. We introduce a systematic thresholding algorithm that leverages topological data analysis to identify optimal network parameters by accounting for higher-order structural relationships. Our method uses persistent homology to compute the stability of homological features across the parameter space, identifying parameter choices that are robust to small variations while preserving meaningful topological structure. Hyperparameters allow users to specify minimum requirements for topological features, effectively constraining the parameter search to avoid spurious solutions. We demonstrate the approach with an application in the Science of Science, where networks of scientific concepts are extracted from research paper abstracts, and concepts are connected when they co-appear in the same abstract. The flexibility of our approach allows researchers to incorporate domain-specific constraints and extends beyond network thresholding to general parameterization problems in data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04884 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Higher-Order Network Structure Inference: A Topological Approach to Network Selection Schroeder, Adam Funk, Russell Guan, Jingyi Okonek, Taylor Ziegelmeier, Lori Social and Information Networks Thresholding--the pruning of nodes or edges based on their properties or weights--is an essential preprocessing tool for extracting interpretable structure from complex network data, yet existing methods face several key limitations. Threshold selection often relies on heuristic methods or trial and error due to large parameter spaces and unclear optimization criteria, leading to sensitivity where small parameter variations produce significant changes in network structure. Moreover, most approaches focus on pairwise relationships between nodes, overlooking critical higher-order interactions involving three or more nodes. We introduce a systematic thresholding algorithm that leverages topological data analysis to identify optimal network parameters by accounting for higher-order structural relationships. Our method uses persistent homology to compute the stability of homological features across the parameter space, identifying parameter choices that are robust to small variations while preserving meaningful topological structure. Hyperparameters allow users to specify minimum requirements for topological features, effectively constraining the parameter search to avoid spurious solutions. We demonstrate the approach with an application in the Science of Science, where networks of scientific concepts are extracted from research paper abstracts, and concepts are connected when they co-appear in the same abstract. The flexibility of our approach allows researchers to incorporate domain-specific constraints and extends beyond network thresholding to general parameterization problems in data analysis. |
| title | Higher-Order Network Structure Inference: A Topological Approach to Network Selection |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2510.04884 |