Evaluating perceptually complementary views for network exploration tasks

Chunlei Chang; Benjamin Bach; Tim Dwyer; Kim Marriott

doi:10.1145/3025453.3026024

Evaluating perceptually complementary views for network exploration tasks

Chunlei Chang
, Benjamin Bach
, Tim Dwyer
, Kim Marriott

Department of Human Centred Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

27 Citations (Scopus)

Abstract

We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.

Original language	English
Title of host publication	Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems
Editors	Cliff Lampe, m.c. schraefel
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	1397-1407
Number of pages	11
ISBN (Print)	9781450346559
DOIs	https://doi.org/10.1145/3025453.3026024
Publication status	Published - 2017
Event	International Conference on Human Factors in Computing Systems 2017 - Colorado Convention Center, Denver, United States of America Duration: 6 May 2017 → 11 May 2017 Conference number: 35th https://chi2017.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3025453 (Proceedings)

Conference

Conference	International Conference on Human Factors in Computing Systems 2017
Abbreviated title	CHI 2017
Country/Territory	United States of America
City	Denver
Period	6/05/17 → 11/05/17
Internet address	https://chi2017.acm.org/ https://dl.acm.org/doi/proceedings/10.1145/3025453 (Proceedings)

Keywords

Event sequence data
Eye tracking
Matrices
Network visualisation
Node-link diagrams
Sankey diagrams

Access to Document

10.1145/3025453.3026024

Cite this

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title = "Evaluating perceptually complementary views for network exploration tasks",

abstract = "We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.",

keywords = "Event sequence data, Eye tracking, Matrices, Network visualisation, Node-link diagrams, Sankey diagrams",

author = "Chunlei Chang and Benjamin Bach and Tim Dwyer and Kim Marriott",

year = "2017",

doi = "10.1145/3025453.3026024",

language = "English",

isbn = "9781450346559",

pages = "1397--1407",

editor = "Cliff Lampe and m.c. schraefel",

booktitle = "Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "International Conference on Human Factors in Computing Systems 2017, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",

url = "https://chi2017.acm.org/, https://dl.acm.org/doi/proceedings/10.1145/3025453",

}

Chang, C, Bach, B, Dwyer, T & Marriott, K 2017, Evaluating perceptually complementary views for network exploration tasks. in C Lampe & MC schraefel (eds), Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), New York NY USA, pp. 1397-1407, International Conference on Human Factors in Computing Systems 2017, Denver, Colorado, United States of America, 6/05/17. https://doi.org/10.1145/3025453.3026024

Evaluating perceptually complementary views for network exploration tasks. / Chang, Chunlei; Bach, Benjamin; Dwyer, Tim et al.
Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems. ed. / Cliff Lampe; m.c. schraefel. New York NY USA: Association for Computing Machinery (ACM), 2017. p. 1397-1407.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Evaluating perceptually complementary views for network exploration tasks

AU - Chang, Chunlei

AU - Bach, Benjamin

AU - Dwyer, Tim

AU - Marriott, Kim

N1 - Conference code: 35th

PY - 2017

Y1 - 2017

N2 - We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.

AB - We explore the relative merits of matrix, node-link and combined side-by-side views for the visualisation of weighted networks with three controlled studies: (1) finding the most effective visual encoding for weighted edges in matrix representations; (2) comparing matrix, node-link and combined views for static weighted networks; and (3) comparing MatrixWave, Sankey and combined views of both for event-sequence data. Our studies underline that node-link and matrix views are suited to different analysis tasks. For the combined view, our studies show that there is a perceptually complementary effect in terms of improved accuracy for some tasks, but that there is a cost in terms of longer completion time than the faster of the two techniques alone. Eye-movement data shows that for many tasks participants strongly favour one of the two views, after trying both in the training phase.

KW - Event sequence data

KW - Eye tracking

KW - Matrices

KW - Network visualisation

KW - Node-link diagrams

KW - Sankey diagrams

UR - https://www.scopus.com/pages/publications/85044872165

U2 - 10.1145/3025453.3026024

DO - 10.1145/3025453.3026024

M3 - Conference Paper

AN - SCOPUS:85044872165

SN - 9781450346559

SP - 1397

EP - 1407

BT - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems

A2 - Lampe, Cliff

A2 - schraefel, m.c.

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

T2 - International Conference on Human Factors in Computing Systems 2017

Y2 - 6 May 2017 through 11 May 2017

ER -

Evaluating perceptually complementary views for network exploration tasks

Abstract

Conference

Keywords

Access to Document

Other files and links

Visualisation of large, complex networks through small, beautiful diagrams

Cite this

Evaluating perceptually complementary views for network exploration tasks

Abstract

Conference

Keywords

Access to Document

Other files and links

Projects

Visualisation of large, complex networks through small, beautiful diagrams

Cite this