The Impact of Graph Layouts on the Perception of Graph Properties

abstract: Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algo...

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Bibliographic Details
Other Authors: Clayton, Benjamin (Author)
Format: Dissertation
Language:English
Published: 2019
Subjects:
JND
Online Access:http://hdl.handle.net/2286/R.I.54992
id ndltd-asu.edu-item-54992
record_format oai_dc
spelling ndltd-asu.edu-item-549922019-11-07T03:01:11Z The Impact of Graph Layouts on the Perception of Graph Properties abstract: Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algorithms best suit human perception for particular graph properties. This thesis explores four different graph properties: average local clustering coefficient (ALCC), global clustering coefficient (GCC), number of triangles (NT), and diameter. For each of these properties, three different graph layouts are applied to represent three different approaches to graph visualization: multidimensional scaling (MDS), force directed (FD), and tsNET. In a series of studies conducted through the crowdsourcing platform Amazon Mechanical Turk, participants are tasked with discriminating between two graphs in order to determine their just noticeable differences (JNDs) for the four graph properties and three layout algorithm pairs. These results are analyzed using previously established methods presented by Rensink et al. and Kay and Heer.The average JNDs are analyzed using a linear model that determines whether the property-layout pair seems to follow Weber's Law, and the individual JNDs are run through a log-linear model to determine whether it is possible to model the individual variance of the participant's JNDs. The models are evaluated using the R2 score to determine if they adequately explain the data and compared using the Mann-Whitney pairwise U-test to determine whether the layout has a significant effect on the perception of the graph property. These tests indicate that the data collected in the studies can not always be modelled well with either the linear model or log-linear model, which suggests that some properties may not follow Weber's Law. Additionally, the layout algorithm is not found to have a significant impact on the perception of some of these properties. Dissertation/Thesis Clayton, Benjamin (Author) Maciejewski, Ross (Advisor) Kobourov, Stephen (Committee member) Sefair, Jorge (Committee member) Arizona State University (Publisher) Computer science Graphs Human perception JND Weber's Law eng 89 pages Masters Thesis Computer Science 2019 Masters Thesis http://hdl.handle.net/2286/R.I.54992 http://rightsstatements.org/vocab/InC/1.0/ 2019
collection NDLTD
language English
format Dissertation
sources NDLTD
topic Computer science
Graphs
Human perception
JND
Weber's Law
spellingShingle Computer science
Graphs
Human perception
JND
Weber's Law
The Impact of Graph Layouts on the Perception of Graph Properties
description abstract: Graphs are commonly used visualization tools in a variety of fields. Algorithms have been proposed that claim to improve the readability of graphs by reducing edge crossings, adjusting edge length, or some other means. However, little research has been done to determine which of these algorithms best suit human perception for particular graph properties. This thesis explores four different graph properties: average local clustering coefficient (ALCC), global clustering coefficient (GCC), number of triangles (NT), and diameter. For each of these properties, three different graph layouts are applied to represent three different approaches to graph visualization: multidimensional scaling (MDS), force directed (FD), and tsNET. In a series of studies conducted through the crowdsourcing platform Amazon Mechanical Turk, participants are tasked with discriminating between two graphs in order to determine their just noticeable differences (JNDs) for the four graph properties and three layout algorithm pairs. These results are analyzed using previously established methods presented by Rensink et al. and Kay and Heer.The average JNDs are analyzed using a linear model that determines whether the property-layout pair seems to follow Weber's Law, and the individual JNDs are run through a log-linear model to determine whether it is possible to model the individual variance of the participant's JNDs. The models are evaluated using the R2 score to determine if they adequately explain the data and compared using the Mann-Whitney pairwise U-test to determine whether the layout has a significant effect on the perception of the graph property. These tests indicate that the data collected in the studies can not always be modelled well with either the linear model or log-linear model, which suggests that some properties may not follow Weber's Law. Additionally, the layout algorithm is not found to have a significant impact on the perception of some of these properties. === Dissertation/Thesis === Masters Thesis Computer Science 2019
author2 Clayton, Benjamin (Author)
author_facet Clayton, Benjamin (Author)
title The Impact of Graph Layouts on the Perception of Graph Properties
title_short The Impact of Graph Layouts on the Perception of Graph Properties
title_full The Impact of Graph Layouts on the Perception of Graph Properties
title_fullStr The Impact of Graph Layouts on the Perception of Graph Properties
title_full_unstemmed The Impact of Graph Layouts on the Perception of Graph Properties
title_sort impact of graph layouts on the perception of graph properties
publishDate 2019
url http://hdl.handle.net/2286/R.I.54992
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