Visual reasoning about social networks using centrality sensitivity

Carlos Correa, Tarik Crnovrsanin, Kwan-Liu Ma

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.

Original languageEnglish (US)
Article number5669304
Pages (from-to)106-120
Number of pages15
JournalIEEE Transactions on Visualization and Computer Graphics
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2012

Fingerprint

Complex networks
Uncertainty analysis
Structural properties
Visualization
Derivatives
Feedback

Keywords

  • centrality
  • eigenvector and Markov importance
  • sensitivity analysis
  • Social network visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

Cite this

Visual reasoning about social networks using centrality sensitivity. / Correa, Carlos; Crnovrsanin, Tarik; Ma, Kwan-Liu.

In: IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 1, 5669304, 01.01.2012, p. 106-120.

Research output: Contribution to journalArticle

@article{c7fe17949d994677b990dd32467cf232,
title = "Visual reasoning about social networks using centrality sensitivity",
abstract = "In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.",
keywords = "centrality, eigenvector and Markov importance, sensitivity analysis, Social network visualization",
author = "Carlos Correa and Tarik Crnovrsanin and Kwan-Liu Ma",
year = "2012",
month = "1",
day = "1",
doi = "10.1109/TVCG.2010.260",
language = "English (US)",
volume = "18",
pages = "106--120",
journal = "IEEE Transactions on Visualization and Computer Graphics",
issn = "1077-2626",
publisher = "IEEE Computer Society",
number = "1",

}

TY - JOUR

T1 - Visual reasoning about social networks using centrality sensitivity

AU - Correa, Carlos

AU - Crnovrsanin, Tarik

AU - Ma, Kwan-Liu

PY - 2012/1/1

Y1 - 2012/1/1

N2 - In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.

AB - In this paper, we study the sensitivity of centrality metrics as a key metric of social networks to support visual reasoning. As centrality represents the prestige or importance of a node in a network, its sensitivity represents the importance of the relationship between this and all other nodes in the network. We have derived an analytical solution that extracts the sensitivity as the derivative of centrality with respect to degree for two centrality metrics based on feedback and random walks. We show that these sensitivities are good indicators of the distribution of centrality in the network, and how changes are expected to be propagated if we introduce changes to the network. These metrics also help us simplify a complex network in a way that retains the main structural properties and that results in trustworthy, readable diagrams. Sensitivity is also a key concept for uncertainty analysis of social networks, and we show how our approach may help analysts gain insight on the robustness of key network metrics. Through a number of examples, we illustrate the need for measuring sensitivity, and the impact it has on the visualization of and interaction with social and other scale-free networks.

KW - centrality

KW - eigenvector and Markov importance

KW - sensitivity analysis

KW - Social network visualization

UR - http://www.scopus.com/inward/record.url?scp=81455154528&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=81455154528&partnerID=8YFLogxK

U2 - 10.1109/TVCG.2010.260

DO - 10.1109/TVCG.2010.260

M3 - Article

VL - 18

SP - 106

EP - 120

JO - IEEE Transactions on Visualization and Computer Graphics

JF - IEEE Transactions on Visualization and Computer Graphics

SN - 1077-2626

IS - 1

M1 - 5669304

ER -