Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model

Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie, Christian Djurhuus, Felix Burmester, Daniel Mathias Holmelund, Monika Frolcová, Morten Mørup
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:11489-11505, 2023.

Abstract

Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of ”us-versus-them” that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the Signed Latent Distance Model (SLDM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks. We further extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes, forming the Signed Latent Relational Distance Model (SLIM). On four real social signed networks of polarization, we demonstrate that the models extract low-dimensional characterizations that well predict friendships and animosity while SLIM provides interpretable visualizations defined by extreme positions when restricting the embedding space to polytopes.

Cite this Paper


BibTeX
@InProceedings{pmlr-v206-nakis23a, title = {Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model}, author = {Nakis, Nikolaos and Celikkanat, Abdulkadir and Boucherie, Louis and Djurhuus, Christian and Burmester, Felix and Holmelund, Daniel Mathias and Frolcov\'a, Monika and M{\o}rup, Morten}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {11489--11505}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/nakis23a/nakis23a.pdf}, url = {https://proceedings.mlr.press/v206/nakis23a.html}, abstract = {Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of ”us-versus-them” that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the Signed Latent Distance Model (SLDM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks. We further extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes, forming the Signed Latent Relational Distance Model (SLIM). On four real social signed networks of polarization, we demonstrate that the models extract low-dimensional characterizations that well predict friendships and animosity while SLIM provides interpretable visualizations defined by extreme positions when restricting the embedding space to polytopes. } }
Endnote
%0 Conference Paper %T Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model %A Nikolaos Nakis %A Abdulkadir Celikkanat %A Louis Boucherie %A Christian Djurhuus %A Felix Burmester %A Daniel Mathias Holmelund %A Monika Frolcová %A Morten Mørup %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-nakis23a %I PMLR %P 11489--11505 %U https://proceedings.mlr.press/v206/nakis23a.html %V 206 %X Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of ”us-versus-them” that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the Signed Latent Distance Model (SLDM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks. We further extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes, forming the Signed Latent Relational Distance Model (SLIM). On four real social signed networks of polarization, we demonstrate that the models extract low-dimensional characterizations that well predict friendships and animosity while SLIM provides interpretable visualizations defined by extreme positions when restricting the embedding space to polytopes.
APA
Nakis, N., Celikkanat, A., Boucherie, L., Djurhuus, C., Burmester, F., Holmelund, D.M., Frolcová, M. & Mørup, M.. (2023). Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:11489-11505 Available from https://proceedings.mlr.press/v206/nakis23a.html.

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