%0 Journal Article %1 han2024probabilistic %A Han, Zhihao %A Liu, Longzhao %A Wang, Xin %A Hao, Yajing %A Zheng, Hongwei %A Tang, Shaoting %A Zheng, Zhiming %D 2024 %I AIP Publishing %J Chaos: An Interdisciplinary Journal of Nonlinear Science %K bistability chaos complexity evolutionary_dynamics mean_field_theory network_theory phase_transitions social_networks topological_properties %N 2 %R 10.1063/5.0167123 %T Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics %U http://dx.doi.org/10.1063/5.0167123 %V 34 %X Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual’s activity rate and the possibility of group interaction, we propose a probabilistic activity-driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems.

@article{han2024probabilistic, abstract = {Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual’s activity rate and the possibility of group interaction, we propose a probabilistic activity-driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. }, added-at = {2024-02-28T13:04:15.000+0100}, author = {Han, Zhihao and Liu, Longzhao and Wang, Xin and Hao, Yajing and Zheng, Hongwei and Tang, Shaoting and Zheng, Zhiming}, biburl = {https://www.bibsonomy.org/bibtex/21b9b351025bc21d16724738afa08cafb/tabularii}, description = {Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics | Chaos: An Interdisciplinary Journal of Nonlinear Science | AIP Publishing}, doi = {10.1063/5.0167123}, interhash = {e69435368d57b4dc4f203d4427e8e961}, intrahash = {1b9b351025bc21d16724738afa08cafb}, issn = {1089-7682}, journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science}, keywords = {bistability chaos complexity evolutionary_dynamics mean_field_theory network_theory phase_transitions social_networks topological_properties}, month = feb, number = 2, publisher = {AIP Publishing}, timestamp = {2024-02-28T13:04:15.000+0100}, title = {Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics}, url = {http://dx.doi.org/10.1063/5.0167123}, volume = 34, year = 2024 }