Evaluation of Visual Network Algorithms on Historical Documents

Visual network is a special type of graph representing real life systems where the vertices are accompanied with attributes and the edges represent relationships between them. Network visualisation facilitate comprehension of texts, especially for historical documents, where important events, facts...

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Main Author:	Khairunnisa, Binti Ibrahim
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://ir.unimas.my/id/eprint/29965/1/Evaluation...pdf
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id	my-unimas-ir.29965
record_format	uketd_dc
institution	Universiti Malaysia Sarawak
collection	UNIMAS Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Khairunnisa, Binti Ibrahim Evaluation of Visual Network Algorithms on Historical Documents
description	Visual network is a special type of graph representing real life systems where the vertices are accompanied with attributes and the edges represent relationships between them. Network visualisation facilitate comprehension of texts, especially for historical documents, where important events, facts and relationships are recorded. This study proposed a generic framework to perform evaluation of visual network algorithms to find the best network representation of a document. The framework suggests to evaluate both graph layout and clustering algorithm in order to produce a good network. The framework has been used to evaluate three graph layout and three graph clustering algorithms on the historical SAGA dataset. The evaluation found that FA2 algorithm when combined with MC algorithm produce the best network representation for SAGA. The evaluation also demonstrates that the scores given by evaluation metrics can disagree with one another as they each are invented based on different opinions on how to indicate a good cluster. The proposed framework is also applied on Biotext and dBPedia dataset and the findings implied that the performance of an algorithm, be it a layout or a clustering algorithm, actually depends on the structure of the document itself. Therefore, for a new document, evaluation of algorithms is ineluctable. The study also proposed a simple but reliable cluster evaluation metric called NPL-C metric. The metric is able to rate both the internal and external structure of clusters in a given network by using the concept of average path length and conductance.
format	Thesis
qualification_level	Master's degree
author	Khairunnisa, Binti Ibrahim
author_facet	Khairunnisa, Binti Ibrahim
author_sort	Khairunnisa, Binti Ibrahim
title	Evaluation of Visual Network Algorithms on Historical Documents
title_short	Evaluation of Visual Network Algorithms on Historical Documents
title_full	Evaluation of Visual Network Algorithms on Historical Documents
title_fullStr	Evaluation of Visual Network Algorithms on Historical Documents
title_full_unstemmed	Evaluation of Visual Network Algorithms on Historical Documents
title_sort	evaluation of visual network algorithms on historical documents
granting_institution	Universiti Malaysia Sarawak (UNIMAS)
granting_department	Faculty of Computer Science and Information Technology
publishDate	2020
url	http://ir.unimas.my/id/eprint/29965/1/Evaluation...pdf
_version_	1783728374015852544
spelling	my-unimas-ir.299652023-06-06T08:31:57Z Evaluation of Visual Network Algorithms on Historical Documents 2020 Khairunnisa, Binti Ibrahim QA75 Electronic computers. Computer science Visual network is a special type of graph representing real life systems where the vertices are accompanied with attributes and the edges represent relationships between them. Network visualisation facilitate comprehension of texts, especially for historical documents, where important events, facts and relationships are recorded. This study proposed a generic framework to perform evaluation of visual network algorithms to find the best network representation of a document. The framework suggests to evaluate both graph layout and clustering algorithm in order to produce a good network. The framework has been used to evaluate three graph layout and three graph clustering algorithms on the historical SAGA dataset. The evaluation found that FA2 algorithm when combined with MC algorithm produce the best network representation for SAGA. The evaluation also demonstrates that the scores given by evaluation metrics can disagree with one another as they each are invented based on different opinions on how to indicate a good cluster. The proposed framework is also applied on Biotext and dBPedia dataset and the findings implied that the performance of an algorithm, be it a layout or a clustering algorithm, actually depends on the structure of the document itself. Therefore, for a new document, evaluation of algorithms is ineluctable. The study also proposed a simple but reliable cluster evaluation metric called NPL-C metric. The metric is able to rate both the internal and external structure of clusters in a given network by using the concept of average path length and conductance. 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Evaluation of Visual Network Algorithms on Historical Documents

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