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Temporal social network modeling of mobile connectivity data with graph neural networks
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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en
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19
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PloS One, Volume 20, issue 12 December, pp. 1-19
Abstract
Graph neural networks (GNNs) have emerged as a state-of-the-art data-driven tool for modeling connectivity data of graph-structured complex networks and integrating information of their nodes and edges in space and time. However, as of yet, the analysis of social networks using the time series of people’s mobile connectivity data has not been extensively investigated. In the present study, we investigate four recently proposed snapshot - based temporal GNNs in predicting the phone call and SMS activity between users of a mobile communication network. In addition, we develop a simple non - GNN baseline model using recently proposed EdgeBank method. Our analysis shows that the ROLAND temporal GNN outperforms the baseline model in most cases, whereas the other three GNNs perform on average worse than the baseline. The results show that GNN based approaches hold promise in the analysis of temporal social networks through mobile connectivity data. However, due to the relatively small performance margin between ROLAND and the baseline model, further research is required on specialized GNN architectures for temporal social network analysis.
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Publisher Copyright: © 2025 Jaskari et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Jaskari, J, Roy, C, Ogushi, F, Saukkoriipi, M, Sahlsten, J & Kaski, K 2025, 'Temporal social network modeling of mobile connectivity data with graph neural networks', PloS One, vol. 20, no. 12 December, e0335267, pp. 1-19. https://doi.org/10.1371/journal.pone.0335267