6533b827fe1ef96bd128681a

RESEARCH PRODUCT

How Correlated Are Community-Aware and Classical Centrality Measures in Complex Networks?

Marinette SavonnetStephany RajehHocine CherifiEric Leclercq

subject

Modularity (networks)Transitive relationTheoretical computer scienceComputer scienceCommunity structureComplex network01 natural sciences[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]010305 fluids & plasmasCorrelationMixing (mathematics)[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]0103 physical sciences[INFO]Computer Science [cs]010306 general physicsCentralitySet (psychology)ComputingMilieux_MISCELLANEOUS

description

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.

yearjournalcountryeditionlanguage
2021-07-30
10.1007/978-3-030-81854-8_11https://hal-univ-bourgogne.archives-ouvertes.fr/hal-03450053