TY  - JOUR
AU  - Maletić, Slobodan
AU  - Zhao, Yi
PY  - 2018
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/1925
AB  - Intricacies of the structure of social relations are realized by representing a collection of overlapping opinions as a simplicial complex, thus building latent multidimensional structures, through which agents are, virtually, moving as they exchange opinions. The influence of opinion space structure on the distribution of opinions is demonstrated by modeling consensus phenomena when the opinion exchange between individuals may be affected by the false consensus effect. The results indicate that in the cases with and without bias, the road toward consensus is influenced by the structure of multidimensional space of opinions, and in the biased case, complete consensus is achieved. The applications of proposed modeling framework can easily be generalized, as they transcend opinion formation modeling. (C) 2017 Elsevier B.V. All rights reserved.
T2  - Physica A: Statistical Mechanics and Its Applications
T1  - Hidden multidimensional social structure modeling applied to biased social perception
VL  - 492
SP  - 1419
EP  - 1430
DO  - 10.1016/j.physa.2017.11.069
ER  - 

@article{
author = "Maletić, Slobodan and Zhao, Yi",
year = "2018",
abstract = "Intricacies of the structure of social relations are realized by representing a collection of overlapping opinions as a simplicial complex, thus building latent multidimensional structures, through which agents are, virtually, moving as they exchange opinions. The influence of opinion space structure on the distribution of opinions is demonstrated by modeling consensus phenomena when the opinion exchange between individuals may be affected by the false consensus effect. The results indicate that in the cases with and without bias, the road toward consensus is influenced by the structure of multidimensional space of opinions, and in the biased case, complete consensus is achieved. The applications of proposed modeling framework can easily be generalized, as they transcend opinion formation modeling. (C) 2017 Elsevier B.V. All rights reserved.",
journal = "Physica A: Statistical Mechanics and Its Applications",
title = "Hidden multidimensional social structure modeling applied to biased social perception",
volume = "492",
pages = "1419-1430",
doi = "10.1016/j.physa.2017.11.069"
}

Maletić, S.,& Zhao, Y.. (2018). Hidden multidimensional social structure modeling applied to biased social perception. in Physica A: Statistical Mechanics and Its Applications, 492, 1419-1430.
https://doi.org/10.1016/j.physa.2017.11.069

Maletić S, Zhao Y. Hidden multidimensional social structure modeling applied to biased social perception. in Physica A: Statistical Mechanics and Its Applications. 2018;492:1419-1430.
doi:10.1016/j.physa.2017.11.069 .

Maletić, Slobodan, Zhao, Yi, "Hidden multidimensional social structure modeling applied to biased social perception" in Physica A: Statistical Mechanics and Its Applications, 492 (2018):1419-1430,
https://doi.org/10.1016/j.physa.2017.11.069 . .

TY  - JOUR
AU  - Maletić, Slobodan
AU  - Zhao, Yi
PY  - 2017
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/1545
AB  - The emergence of complex datasets permeates versatile research disciplines leading to the necessity to develop methods for tackling complexity through finding the patterns inherent in datasets. The challenge lies in transforming the extracted patterns into pragmatic knowledge. In this paper, new information entropy measures for the characterization of the multidimensional structure extracted from complex datasets are proposed, complementing the conventionally-applied algebraic topology methods. Derived from topological relationships embedded in datasets, multilevel entropy measures are used to track transitions in building the high dimensional structure of datasets captured by the stratified partition of a simplicial complex. The proposed entropies are found suitable for defining and operationalizing the intuitive notions of structural relationships in a cumulative experience of a taxi drivers cognitive map formed by origins and destinations. The comparison of multilevel integration entropies calculated after each new added ride to the data structure indicates slowing the pace of change over time in the origin-destination structure. The repetitiveness in taxi driver rides, and the stability of origin-destination structure, exhibits the relative invariance of rides in space and time. These results shed light on taxi drivers ride habits, as well as on the commuting of persons whom he/she drove.
T2  - Entropy
T1  - Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets
VL  - 19
IS  - 4
SP  - 172
DO  - 10.3390/e19040172
ER  - 

@article{
author = "Maletić, Slobodan and Zhao, Yi",
year = "2017",
abstract = "The emergence of complex datasets permeates versatile research disciplines leading to the necessity to develop methods for tackling complexity through finding the patterns inherent in datasets. The challenge lies in transforming the extracted patterns into pragmatic knowledge. In this paper, new information entropy measures for the characterization of the multidimensional structure extracted from complex datasets are proposed, complementing the conventionally-applied algebraic topology methods. Derived from topological relationships embedded in datasets, multilevel entropy measures are used to track transitions in building the high dimensional structure of datasets captured by the stratified partition of a simplicial complex. The proposed entropies are found suitable for defining and operationalizing the intuitive notions of structural relationships in a cumulative experience of a taxi drivers cognitive map formed by origins and destinations. The comparison of multilevel integration entropies calculated after each new added ride to the data structure indicates slowing the pace of change over time in the origin-destination structure. The repetitiveness in taxi driver rides, and the stability of origin-destination structure, exhibits the relative invariance of rides in space and time. These results shed light on taxi drivers ride habits, as well as on the commuting of persons whom he/she drove.",
journal = "Entropy",
title = "Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets",
volume = "19",
number = "4",
pages = "172",
doi = "10.3390/e19040172"
}

Maletić, S.,& Zhao, Y.. (2017). Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets. in Entropy, 19(4), 172.
https://doi.org/10.3390/e19040172

Maletić S, Zhao Y. Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets. in Entropy. 2017;19(4):172.
doi:10.3390/e19040172 .

Maletić, Slobodan, Zhao, Yi, "Multilevel Integration Entropies: The Case of Reconstruction of Structural Quasi-Stability in Building Complex Datasets" in Entropy, 19, no. 4 (2017):172,
https://doi.org/10.3390/e19040172 . .