Finding Dominant Nodes Using Graphlets

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Aparício, D (Author) Other; Pedro Ribeiro (Author) FCUP; Silva, F (Author) FCUP; Silva, JMB (Author) Other

Conference proceedings International

Title: Complex Networks and Their Applications VIII - Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Pages: 77-89

8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019

10 December 2019 through 12 December 2019

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00R-GH6

DOI: 10.1007/978-3-030-36687-2_7

Abstract (EN): 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. © 2020, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific