How Correlated are Community-aware and Classical Centrality Measures in Complex Networks?
arXiv:2201.12918 · doi:10.1007/978-3-030-81854-8_11
Abstract
Unlike classical centrality measures, recently developed community-aware centrality measures use a network's community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classical and community-aware centrality measures generally exhibit low to medium correlation values. These results are consistent across networks. Transitivity and efficiency are the most influential macroscopic network features driving the correlation variation between classical and community-aware centrality measures. Additionally, the mixing parameter, the modularity, and the Max-ODF are the main mesoscopic topological properties exerting the most substantial effect.
arXiv commentAccepted in International Conference on Complex Networks: CompleNet-Live 2021
References in corpus (13)
- Functional cartography of complex metabolic networks
- Vital nodes identification in complex networks
- Consistency and differences between centrality measures across distinct classes of networks
- Correlation between centrality metrics and their application to the opinion model
- Measuring Node Contribution to Community Structure with Modularity Vitality
- M-Centrality: identifying key nodes based on global position and local degree variation
- Qualitative Comparison of Community Detection Algorithms
- Analyzing complex networks through correlations in centrality measurements
- Interplay Between Hierarchy and Centrality in Complex Networks
- An Empirical Study of the Relation Between Community Structure and Transitivity
- Community-based Immunization Strategies for Epidemic Control
- Handbook of Network Analysis [KONECT -- the Koblenz Network Collection]
- Investigating Centrality Measures in Social Networks with Community Structure