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Block matrix models for dynamic networks

Author

Listed:
  • Al Mugahwi, Mohammed
  • De La Cruz Cabrera, Omar
  • Fenu, Caterina
  • Reichel, Lothar
  • Rodriguez, Giuseppe

Abstract

Networks in which connections change over time arise in many applications, e.g., when modeling phone calls and flights between airports. This paper discusses new ways to define adjacency matrices associated with this kind of networks. We propose that dynamic networks be modeled with the aid of block upper triangular adjacency matrices. Both modeling and computational aspects are discussed. Several applications to real dynamic networks are presented and illustrate the advantages of the proposed method when compared with an available approach.

Suggested Citation

  • Al Mugahwi, Mohammed & De La Cruz Cabrera, Omar & Fenu, Caterina & Reichel, Lothar & Rodriguez, Giuseppe, 2021. "Block matrix models for dynamic networks," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    • Handle: RePEc:eee:apmaco:v:402:y:2021:i:c:s0096300321001697
    DOI: 10.1016/j.amc.2021.126121
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    References listed on IDEAS

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    1. Alsayed, Ahmad & Higham, Desmond J., 2015. "Betweenness in time dependent networks," Chaos, Solitons & Fractals, Elsevier, vol. 72(C), pages 35-48.
    2. Daniel Kressner & Robert Luce & Francesco Statti, 2017. "Incremental computation of block triangular matrix exponentials with application to option pricing," Papers 1703.00182, arXiv.org, revised Jun 2017.
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    Cited by:

    1. Li, Xianghua & Zhen, Xiyuan & Qi, Xin & Han, Huichun & Zhang, Long & Han, Zhen, 2023. "Dynamic community detection based on graph convolutional networks and contrastive learning," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).

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