Abstract
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.
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Acknowledgements
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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AparÃcio, D., Ribeiro, P., Silva, F., Silva, J. (2020). Finding Dominant Nodes Using Graphlets. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_7
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