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 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 expone

SUBMITTER: Lee HW 

PROVIDER: S-EPMC11746983 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature