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Density centrality: identifying influential nodes based on area density formula

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  • Ibnoulouafi, Ahmed
  • El Haziti, Mohamed

Abstract

Identifying central nodes in a network is crucial to accelerate or contain the spreading of information such as diseases and rumors. The problem is formulated as follows, given a complex network, which node(s) is (are) the more important ? The idea of centrality was initially introduced in the context of sociology, to look whether there is a relation between the location of an individual in the network and its influence in group processes. Since then, a plethora of centrality measures has emerged over the years and were employed in a multitude of contexts to rank nodes according to their topological importance. Each centrality targets the problem of influence from its own perspective.

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  • Ibnoulouafi, Ahmed & El Haziti, Mohamed, 2018. "Density centrality: identifying influential nodes based on area density formula," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 69-80.
    • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:69-80
    DOI: 10.1016/j.chaos.2018.06.022
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    References listed on IDEAS

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    1. Pagani, Giuliano Andrea & Aiello, Marco, 2014. "Power grid complex network evolutions for the smart grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 248-266.
    2. Estrada, Ernesto & Hatano, Naomichi, 2010. "A vibrational approach to node centrality and vulnerability in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3648-3660.
    3. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    4. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    5. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    6. Lordan, Oriol & Sallan, Jose M. & Simo, Pep, 2014. "Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda," Journal of Transport Geography, Elsevier, vol. 37(C), pages 112-120.
    7. Liu, Jun & Xiong, Qingyu & Shi, Weiren & Shi, Xin & Wang, Kai, 2016. "Evaluating the importance of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 209-219.
    8. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    9. Ma, Ling-ling & Ma, Chuang & Zhang, Hai-Feng & Wang, Bing-Hong, 2016. "Identifying influential spreaders in complex networks based on gravity formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 205-212.
    10. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
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    Cited by:

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    2. Yat Yen & Pengjun Zhao & Muhammad T Sohail, 2021. "The morphology and circuity of walkable, bikeable, and drivable street networks in Phnom Penh, Cambodia," Environment and Planning B, , vol. 48(1), pages 169-185, January.

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