Signed graphs in data sciences via communicability geometry
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866908273795399680 |
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| author | Diaz-Diaz, Fernando Estrada, Ernesto |
| author_facet | Diaz-Diaz, Fernando Estrada, Ernesto |
| contents | Signed graphs are an emergent way of representing data in a variety of contexts where antagonistic interactions exist. These include data from biological, ecological, and social systems. Here we propose the concept of communicability for signed graphs and explore in depth its mathematical properties. We also prove that the communicability induces a hyperspherical geometric embedding of the signed network, and derive communicability-based metrics that satisfy the axioms of a distance even in the presence of negative edges. We then apply these metrics to solve several problems in the data analysis of signed graphs within a unified framework. These include the partitioning of signed graphs, dimensionality reduction, finding hierarchies of alliances in signed networks, and quantifying the degree of polarization between the existing factions in social systems represented by these types of graphs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_07493 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Signed graphs in data sciences via communicability geometry Diaz-Diaz, Fernando Estrada, Ernesto Metric Geometry Discrete Mathematics Machine Learning Combinatorics Physics and Society Signed graphs are an emergent way of representing data in a variety of contexts where antagonistic interactions exist. These include data from biological, ecological, and social systems. Here we propose the concept of communicability for signed graphs and explore in depth its mathematical properties. We also prove that the communicability induces a hyperspherical geometric embedding of the signed network, and derive communicability-based metrics that satisfy the axioms of a distance even in the presence of negative edges. We then apply these metrics to solve several problems in the data analysis of signed graphs within a unified framework. These include the partitioning of signed graphs, dimensionality reduction, finding hierarchies of alliances in signed networks, and quantifying the degree of polarization between the existing factions in social systems represented by these types of graphs. |
| title | Signed graphs in data sciences via communicability geometry |
| topic | Metric Geometry Discrete Mathematics Machine Learning Combinatorics Physics and Society |
| url | https://arxiv.org/abs/2403.07493 |