Abstract
Two approaches to clustering and blockmodeling of temporal networks are presented: the first is based on an adaptation of the clustering of symbolic data described by modal values and the second is based on clustering with relational constraints. Different options for describing a temporal block model are discussed.
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Acknowledgements
The paper contains an elaborated version of ideas presented in my talks at the XXXX Sunbelt Social Networks Conference (on Zoom), July 13-17, 2020 and at the EUSN 2021—5th European Conference on Social Networks, Naples (on Zoom), September 6-10, 2021. This work is supported in part by the Slovenian Research Agency (research program P1-0294 and research projects J1-9187, J1-2481, and J5-2557), and prepared within the framework of the HSE University Basic Research Program.
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Batagelj, V. (2023). Clustering and Blockmodeling Temporal Networks – Two Indirect Approaches. In: Brito, P., Dias, J.G., Lausen, B., Montanari, A., Nugent, R. (eds) Classification and Data Science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_8
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