Semantic Centrality for Temporal Graphs
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42941-5_15
Reference8 articles.
1. 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
2. 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
3. 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
4. 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
5. 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
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