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The aggregation of multiplex networks based on the similarity of networks

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  • Li, Liqiang
  • Liu, Jing

Abstract

Many complex systems can be represented as networks composed of different types of interactions, which are categorized as links belonging to different layers, forming multiplex networks (MNs). In order to simplify the study of MNs, many methods for aggregating MNs have been proposed, but the aggregation of MNs still faces two challenges. The first one is how to measure the destruction of original MN’s structure after they are aggregated and the other is how to meet different requirements for the integrity of network structures for different studies. For the first one, we design an efficient and novelty index named as similarity of MNs (SMNs) for measuring the MN’s structural similarity between original and simplified MNs. For the second one, a method for aggregating MNs, named as AMNs, is proposed to compromise the simplification and maintain the core structure of MNs. Several representative synthetic networks are used to evaluate the reliability of AMNs. Moreover, AMNs is applied to some real-life MNs, including biology, human society, and transportation MNs. Experimental results demonstrate positively that the proposed approach AMNs can simplify the MNs effectively under different requirements.

Suggested Citation

  • Li, Liqiang & Liu, Jing, 2020. "The aggregation of multiplex networks based on the similarity of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s037843711931684x
    DOI: 10.1016/j.physa.2019.122976
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    References listed on IDEAS

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    3. Wang, Xiaodong & Liu, Jing, 2017. "A layer reduction based community detection algorithm on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 244-252.
    4. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
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