Triadic balance and network evolution in predictive models of signed networks
Abstract This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving pattern...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-85078-5 |
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| _version_ | 1832585707167678464 |
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| author | Hsuan-Wei Lee Pei-Chin Lu Hsiang-Chuan Sha Hsini Huang |
| author_facet | Hsuan-Wei Lee Pei-Chin Lu Hsiang-Chuan Sha Hsini Huang |
| author_sort | Hsuan-Wei Lee |
| collection | DOAJ |
| description | Abstract This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research. |
| format | Article |
| id | doaj-art-538075e3a1264244a18cdd208264ace6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-538075e3a1264244a18cdd208264ace62025-01-26T12:33:38ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-024-85078-5Triadic balance and network evolution in predictive models of signed networksHsuan-Wei Lee0Pei-Chin Lu1Hsiang-Chuan Sha2Hsini Huang3Lehigh UniversityUniversity of ChicagoVirtual Science AILeiden UniversityAbstract This paper introduces a novel approach for identifying dynamic triadic transformation processes, applied to five networks: three undirected and two directed. Our method significantly enhances the prediction accuracy of network ties. While balance theory offers insights into evolving patterns of triadic structures, its effects on overall network dynamics remain underexplored. Existing research often neglects the interaction between micro-level balancing mechanisms and overall network behavior. To bridge this gap, we develop a method for detecting dynamic triadic structures in signed networks, categorizing triangle transformations over two consecutive periods into formation and breakage. We analyze the impact of these structures on temporal network evolution by incorporating them into exponential random graph models across five networks of varying size, density, and directionality. To address the complexity of multi-layer networks derived from signed networks, we modify the temporal exponential random graph model framework. Our method significantly improves out-of-sample prediction accuracy for network ties, with additional predictive power from incorporating negative network information. These findings highlight the importance of considering the triadic transformation processes of balance triangles in studying temporal networks, validated across diverse datasets, warranting further research.https://doi.org/10.1038/s41598-024-85078-5 |
| spellingShingle | Hsuan-Wei Lee Pei-Chin Lu Hsiang-Chuan Sha Hsini Huang Triadic balance and network evolution in predictive models of signed networks Scientific Reports |
| title | Triadic balance and network evolution in predictive models of signed networks |
| title_full | Triadic balance and network evolution in predictive models of signed networks |
| title_fullStr | Triadic balance and network evolution in predictive models of signed networks |
| title_full_unstemmed | Triadic balance and network evolution in predictive models of signed networks |
| title_short | Triadic balance and network evolution in predictive models of signed networks |
| title_sort | triadic balance and network evolution in predictive models of signed networks |
| url | https://doi.org/10.1038/s41598-024-85078-5 |
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