Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities

Abstract Among the properties describing the behavior of centrality measures with respect to network modifications, score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target with respect to the remaining nodes. It is known (Boldi and Vigna Intern Math 10:222–262, 2014, Boldi et al. Netw Sci 5(4):529–550, 2017) that score and rank monotonicity hold in directed graphs for almost all the classical centrality measures. In undirected graphs one expects that the corresponding properties hold when adding a new edge—in this case, both endpoints of the new edge should enjoy the increase in score/rank. However, recent results (Boldi et al. in Netw Sci 11(3):1–23, 2023) have shown that this is not true: for many centrality measures, it is possible to find situations in which adding an edge reduces the rank of one of its two endpoints. In this paper we introduce a weaker property for undirected networks, semi-monotonicity , in which just one of the two endpoints of a new edge is required to enjoy score or rank monotonicity. We show that this property is satisfied by closeness centrality, by a large class of distance-based centralities, and (somehow surprisingly) by betweenness centrality. In the last two cases, we prove in fact a stronger property, basin dominance , which is of independent interest.

Authors

Institutions

Publication Details

Journal
Social Network Analysis and Mining
Published
2024-09-06
DOI
https://doi.org/10.1007/s13278-024-01285-y
Citations
4
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.12

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities

Sebastiano Vigna, Paolo Boldi, Flavio Furia, Davide D’Ascenzo
4 citations
Social Network Analysis and Mining
Complex Network Analysis Techniques
1.12
article

Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities

Sebastiano Vigna, Paolo Boldi, Flavio Furia, Davide D’Ascenzo
article en
4 citations

Abstract

Abstract Among the properties describing the behavior of centrality measures with respect to network modifications, score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target with respect to the remaining nodes. It is known (Boldi and Vigna Intern Math 10:222–262, 2014, Boldi et al. Netw Sci 5(4):529–550, 2017) that score and rank monotonicity hold in directed graphs for almost all the classical centrality measures. In undirected graphs one expects that the corresponding properties hold when adding a new edge—in this case, both endpoints of the new edge should enjoy the increase in score/rank. However, recent results (Boldi et al. in Netw Sci 11(3):1–23, 2023) have shown that this is not true: for many centrality measures, it is possible to find situations in which adding an edge reduces the rank of one of its two endpoints. In this paper we introduce a weaker property for undirected networks, semi-monotonicity , in which just one of the two endpoints of a new edge is required to enjoy score or rank monotonicity. We show that this property is satisfied by closeness centrality, by a large class of distance-based centralities, and (somehow surprisingly) by betweenness centrality. In the last two cases, we prove in fact a stronger property, basin dominance , which is of independent interest.

Social Network Analysis and MiningVol. 14(1)
University of Milan (IT)
Università degli Studi di Milano, European Commission
Openalex Percentile: Top 23%
Complex Network Analysis Techniques
1.12
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Score and rank semi-monotonicity for closeness, betweenness, and distance–decay centralities — Sebastiano Vigna, Paolo Boldi, et al. · Social Network Analysis and Mining (2024) | TGRS Research Map | TGRS