Abstract
Centrality metrics in graphs, such as degree, help to understand the influence of entities in various applications. They are used to quantify the influence of entities based on their relationships. Time dimension has been integrated to take into account the evolution of relationships between entities in real-world phenomena. For instance, in the context of disease spreading, new contacts may appear and disappear over time between individuals. However, they do not take into account the semantics of entities and their relationships. For example, in the context of a disease spreading, some relationships (such as physical contacts) may be more important than others (such as virtual contacts). To overcome this drawback, we propose centrality metrics that integrate both temporal and semantics aspects. We carry out experimental assessments, with real-world datasets, to illustrate the efficiency of our solution.
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Andriamampianina, L., Ravat, F., Song, J., Vallès-Parlangeau, N.: Graph data temporal evolutions: from conceptual modelling to implementation. Data Knowl. Eng. 139, 102017 (2022). https://doi.org/10.1016/j.datak.2022.102017
Andriamampianina, L., Ravat, F., Song, J., Vallès-Parlangeau, N.: Querying temporal property graphs. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds.) CAiSE 2022, pp. 355–370. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-07472-1_21
Ghanem, M., Magnien, C., Tarissan, F.: Centrality metrics in dynamic networks: a comparison study. IEEE Trans. Network Sci. Eng. 6(4), 940–951 (2019). https://doi.org/10.1109/TNSE.2018.2880344
Ishfaq, U., Khan, H.U., Iqbal, S.: Identifying the influential nodes in complex social networks using centrality-based approach. J. King Saud Univ. Comput. Inf. Sci. 34(10, Part B), 9376–9392 (2022). https://doi.org/10.1016/j.jksuci.2022.09.016
Meng, Y., Qi, Q., Liu, J., Zhou, W.: Dynamic evolution analysis of complex topology and node importance in Shenzhen metro network from 2004 to 2021. Sustainability 14(12), 7234 (2022). https://doi.org/10.3390/su14127234
Rost, C., Gomez, K., Christen, P., Rahm, E.: Evolution of degree metrics in large temporal graphs (2023). https://doi.org/10.18420/BTW2023-23
Uddin, S., Piraveenan, M., Chung, K.S.K., Hossain, L.: Topological analysis of longitudinal networks. In: 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, January 2013, pp. 3931–3940. IEEE (2013). https://doi.org/10.1109/HICSS.2013.556
Wan, Z., Mahajan, Y., Kang, B.W., Moore, T.J., Cho, J.H.: A survey on centrality metrics and their network resilience analysis. IEEE Access 9, 104773–104819 (2021). https://doi.org/10.1109/ACCESS.2021.3094196
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Andriamampianina, L., Ravat, F., Song, J., Vallès-Parlangeau, N. (2023). Semantic Centrality for Temporal Graphs. In: Abelló, A., et al. New Trends in Database and Information Systems. ADBIS 2023. Communications in Computer and Information Science, vol 1850. Springer, Cham. https://doi.org/10.1007/978-3-031-42941-5_15
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DOI: https://doi.org/10.1007/978-3-031-42941-5_15
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