Finding Dominant Nodes Using Graphlets

  • David AparícioEmail author
  • Pedro Ribeiro
  • Fernando Silva
  • Jorge Silva
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


Finding important nodes is a classic task in network science. Nodes are important depending on the context; e.g., they can be (i) nodes that, when removed, cause the network to collapse or (ii) influential spreaders (e.g., of information, or of diseases). Typically, central nodes are assumed to be important, and numerous network centrality measures have been proposed such as the degree centrality, the betweenness centrality, and the subgraph centrality. However, centrality measures are not tailored to capture one particular kind of important nodes: dominant nodes. We define dominant nodes as nodes that dominate many others and are not dominated by many others. We then propose a general graphlet-based measure of node dominance called graphlet-dominance (GD). We analyze how GD differs from traditional network centrality measures. We also study how certain parameters (namely the importance of dominating versus not being dominated and indirect versus direct dominances) influence GD. Finally, we apply GD to author ranking and verify that GD is superior to PageRank in four of the five citation networks tested.


Graphlets Node centrality Node dominance PageRank 



This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project: UID/EEA/50014/2019. Jorge Silva is supported by a FCT/MAP-i PhD grant (PD/BD/128157/2016).


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • David Aparício
    • 1
    Email author
  • Pedro Ribeiro
    • 1
  • Fernando Silva
    • 1
  • Jorge Silva
    • 1
  1. 1.CRACS & INESC-TEC and the Department of Computer Science, Faculty of SciencesUniversity of PortoPortoPortugal

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