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Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks

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Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops (AIAI 2021)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 628))

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Abstract

In social network analysis, it is crucial to discover a community through the retrospective decomposition of a large social graph into easily interpretable subgraphs. Four major community discovery algorithms, namely the Breadth-First Search, the Louvain, the MaxToMin, and the Propinquity Dynamics, are implemented. Their correctness was functionally evaluated in the four most widely used graphs with vastly different characteristics and a dataset retrieved from Twitter regarding cultural and natural heritage data because this platform reflects public perception about historical events through means such as advanced storytelling in users timelines. The primary finding was that the Propinquity Dynamics algorithm outperforms the other algorithms in terms of NMI for most graphs. In contrast, this algorithm with the Louvain performs almost the same regarding modularity.

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Acknowledgement

This research has been co-financed by the European Union and Greek national funds through the Competitiveness, Entrepreneurship and Innovation Operational Programme, under the Call “Research - Create - Innovate", project title: “Using Digital Tools and Applications for Outdoor Alternative Tourism Operators - DIMOLEON", project code: T2EDK-03168, MIS code: 5069920.

Author information

Authors and Affiliations

  1. Department of Regional Development, Ionian University, Lefkada, Greece

    Andreas Kanavos

  2. Computer Engineering and Informatics Department, University of Patras, Patras, Greece

    Andreas Kanavos, Maria Trigka, Elias Dritsas & Gerasimos Vonitsanos

  3. Department of Informatics, Ionian University, Corfu, Greece

    Phivos Mylonas

  4. Institute of Entrepreneurship Development, Larissa, Greece

    Phivos Mylonas

Authors
  1. Andreas Kanavos
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  2. Maria Trigka
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  3. Elias Dritsas
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  4. Gerasimos Vonitsanos
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  5. Phivos Mylonas
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Corresponding author

Correspondence to Andreas Kanavos .

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Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. University of Sunderland, Sunderland, UK

    John Macintyre

  3. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

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Kanavos, A., Trigka, M., Dritsas, E., Vonitsanos, G., Mylonas, P. (2021). Community Detection Algorithms for Cultural and Natural Heritage Data in Social Networks. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021 IFIP WG 12.5 International Workshops. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-030-79157-5_32

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  • DOI: https://doi.org/10.1007/978-3-030-79157-5_32

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