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Agent-Based Vector-Label Propagation for Explaining Social Network Structures

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Knowledge Management in Organisations (KMO 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1593))

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Abstract

Even though Social Network Analysis is quite helpful in studying the structural properties of interconnected systems, real-world networks reveal much more hidden characteristics from interacting domain-specific features. In this study, we designed an Agent-based Vector-label PRopagation Algorithm (AVPRA), which captures both structural properties and domain-specific features of a given network by assigning vectors of features to constituting agents. Experimental analysis proves that our algorithm is accurate in revealing the structural properties of a network in an explainable fashion. Furthermore, the resulting vector-labels are suitable for downstream machine learning tasks.

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Notes

  1. 1.

    The networks are created using the powerlaw_cluster_graph function of NetworkX package 2, which is based on Holme and Kim algorithm [18].

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Acknowledgements

This work was supported by the Università degli Studi di Milano under the Seal of Excellence (SoE) SEED 2020 Project POPULITE - POPUlist Language in ITalian political Elites (Project ID 1090).

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Authors and Affiliations

  1. Università degli Studi di Milano (UNIMI), Milan, Italy

    Valerio Bellandi, Paolo Ceravolo & Samira Maghool

  2. Khalifa University (KUST), Abu Dhabi, UAE

    Ernesto Damiani

Authors
  1. Valerio Bellandi
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  2. Paolo Ceravolo
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  3. Ernesto Damiani
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  4. Samira Maghool
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Corresponding authors

Correspondence to Paolo Ceravolo or Samira Maghool .

Editor information

Editors and Affiliations

  1. Staffordshire University, Stoke-on-Trent, UK

    Lorna Uden

  2. University of Kaohsiung, Kaohsiung, Taiwan

    I-Hsien Ting

  3. FernUniversität in Hagen, Hagen, Germany

    Birgit Feldmann

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Bellandi, V., Ceravolo, P., Damiani, E., Maghool, S. (2022). Agent-Based Vector-Label Propagation for Explaining Social Network Structures. In: Uden, L., Ting, IH., Feldmann, B. (eds) Knowledge Management in Organisations. KMO 2022. Communications in Computer and Information Science, vol 1593. Springer, Cham. https://doi.org/10.1007/978-3-031-07920-7_24

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  • DOI: https://doi.org/10.1007/978-3-031-07920-7_24

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  • Online ISBN: 978-3-031-07920-7

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