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Network Analysis Based on Important Node Selection and Community Detection

Author

Listed:
  • Attila Mester

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

  • Andrei Pop

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

  • Bogdan-Eduard-Mădălin Mursa

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

  • Horea Greblă

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

  • Laura Dioşan

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

  • Camelia Chira

    (Department of Computer Science, Babeş-Bolyai University, 400084 Cluj-Napoca, Romania)

Abstract

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

Suggested Citation

  • Attila Mester & Andrei Pop & Bogdan-Eduard-Mădălin Mursa & Horea Greblă & Laura Dioşan & Camelia Chira, 2021. "Network Analysis Based on Important Node Selection and Community Detection," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2294-:d:637546
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    References listed on IDEAS

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    1. Roger Guimerà & Luís A. Nunes Amaral, 2005. "Functional cartography of complex metabolic networks," Nature, Nature, vol. 433(7028), pages 895-900, February.
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    2. Natalia Markovich & Maksim Ryzhov & Marijus Vaičiulis, 2022. "Tail Index Estimation of PageRanks in Evolving Random Graphs," Mathematics, MDPI, vol. 10(16), pages 1-26, August.
    3. Jun Zhao & Wenyu Rong & Di Liu, 2023. "Urban Agglomeration High-Speed Railway Backbone Network Planning: A Case Study of Beijing-Tianjin-Hebei Region, China," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    4. Zuraida Abal Abas & Mohd Natashah Norizan & Zaheera Zainal Abidin & Ahmad Fadzli Nizam Abdul Rahman & Hidayah Rahmalan & Ida Hartina Ahmed Tharbe & Wan Farah Wani Wan Fakhruddin & Nurul Hafizah Mohd Z, 2022. "Modeling Physical Interaction and Understanding Peer Group Learning Dynamics: Graph Analytics Approach Perspective," Mathematics, MDPI, vol. 10(9), pages 1-18, April.

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