6533b857fe1ef96bd12b3a21

RESEARCH PRODUCT

Investigating Centrality Measures in Social Networks with Community Structure

Hocine CherifiMarinette SavonnetEric LeclercqStephany Rajeh

subject

Bridging (networking)Social networkExploitbusiness.industryComputer scienceNode (networking)Community structure02 engineering and technologyComplex networkData science[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI][INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]020204 information systemsSimilarity (psychology)0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingbusinessCentralityComputingMilieux_MISCELLANEOUS

description

Centrality measures are crucial in quantifying the influence of the members of a social network. Although there has been a great deal of work dealing with this issue, the vast majority of classical centrality measures are agnostic of the community structure characterizing many social networks. Recent works have developed community-aware centrality measures that exploit features of the community structure information encountered in most real-world complex networks. In this paper, we investigate the interactions between 5 popular classical centrality measures and 5 community-aware centrality measures using 8 real-world online networks. Correlation as well as similarity measures between both types of centrality measures are computed. Results show that community-aware centrality measures can be divided into two groups. The first group, which includes Bridging centrality, Community Hub-Bridge, and Participation Coefficient, provides distinctive node information as compared to classical centrality. This behavior is consistent across the networks. The second group which includes Community-based Mediator and Number of Neighboring Communities is characterized by more mixed results that vary across networks.

yearjournalcountryeditionlanguage
2021-12-20
https://doi.org/10.1007/978-3-030-65347-7_18