A framework to quantify approximate simulation on graph data

Chen, X; Lai, L; Qin, L; Lin, X; Liu, B

A framework to quantify approximate simulation on graph data

Chen, X Lai, L Qin, L

Lin, X Liu, B

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Publisher:: IEEE
Publication Type:: Journal Article
Citation:: Proceedings - International Conference on Data Engineering, 2021, 2021-April, pp. 1308-1319
Issue Date:: 2021-04-01

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Field	Value	Language
dc.contributor.author	Chen, X
dc.contributor.author	Lai, L
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062
dc.contributor.author	Lin, X
dc.contributor.author	Liu, B
dc.date.accessioned	2022-07-04T22:41:35Z
dc.date.available	2022-07-04T22:41:35Z
dc.date.issued	2021-04-01
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2021, 2021-April, pp. 1308-1319
dc.identifier.isbn	9781728191843
dc.identifier.issn	1084-4627
dc.identifier.uri	http://hdl.handle.net/10453/158629
dc.description.abstract	Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse "yes-or-no"indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional χ-simulation measure to quantify the degree to which one node simulates another by the simulation variant χ. To this end, we first present several properties necessary for a fractional χ-simulation measure. Then, we present FSimχ, a general fractional χ-simulation computation framework that can be configured to quantify the extent of all χ-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/DP180103096
dc.relation	http://purl.org/au-research/grants/arc/FT200100787
dc.relation.ispartof	Proceedings - International Conference on Data Engineering
dc.relation.isbasedon	10.1109/ICDE51399.2021.00117
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A framework to quantify approximate simulation on graph data
dc.type	Journal Article
utslib.citation.volume	2021-April
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-04T22:41:30Z
pubs.publication-status	Published
pubs.volume	2021-April

Abstract:

Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse "yes-or-no"indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional χ-simulation measure to quantify the degree to which one node simulates another by the simulation variant χ. To this end, we first present several properties necessary for a fractional χ-simulation measure. Then, we present FSimχ, a general fractional χ-simulation computation framework that can be configured to quantify the extent of all χ-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient.

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http://hdl.handle.net/10453/158629