Graph structures nowadays pervasive Big Data. It is often useful to regroup suchclusters data incan clusters, according distinctive node features, and use are a representative elementinfor each cluster. In many real-world cases, be identified by to a set of connected features, and share use a representative element for eachfunction, cluster. Ini.e. many real-world cases, clusters be identified byrepresentation a set of connected vertices that the result of some categorical a mapping of the vertices intocan some categorical that vertices that in share the set result of some categorical function, a mappingterrains of the with vertices into some categorical that takes values a finite C. As an example, we can identifyi.e. contiguous the same discrete propertyrepresentation on a geographical takes values in a finite set C. As an example, we can identify contiguous terrains with the same discrete property on a geographical map, leveraging Space Syntax. In this case, thematic areas within cities are labelled with different colors and color zones are map, leveraging Space Syntax. In this areas withinContracted cities are labelled with different zones are analysed by means of their structure andcase, theirthematic mutual interactions. graphs can help identifycolors issuesand andcolor characteristics analysed by means of their structure and their mutual interactions. Contracted graphs can help identify issues and characteristics of the original structures that were not visible before. of This the original structures and thatdiscusses were not visible before. paper introduces the problem of contracting possibly large colored graphs into much smaller representatives. Thisprovides paper introduces and discusses the problem of contracting graphs into much representatives. It also a novel serial but parallelizable algorithm to tackle possibly this task.large Somecolored initial performance plots smaller are given and discussed It also provides a novel serial but parallelizable algorithm to tackle this task. Some initial performance plots are given and discussed together with hints for future development. together with hints for future development.

Graph Contraction on Attribute-Based Coloring

Lombardi;Flavio;
2022

Abstract

Graph structures nowadays pervasive Big Data. It is often useful to regroup suchclusters data incan clusters, according distinctive node features, and use are a representative elementinfor each cluster. In many real-world cases, be identified by to a set of connected features, and share use a representative element for eachfunction, cluster. Ini.e. many real-world cases, clusters be identified byrepresentation a set of connected vertices that the result of some categorical a mapping of the vertices intocan some categorical that vertices that in share the set result of some categorical function, a mappingterrains of the with vertices into some categorical that takes values a finite C. As an example, we can identifyi.e. contiguous the same discrete propertyrepresentation on a geographical takes values in a finite set C. As an example, we can identify contiguous terrains with the same discrete property on a geographical map, leveraging Space Syntax. In this case, thematic areas within cities are labelled with different colors and color zones are map, leveraging Space Syntax. In this areas withinContracted cities are labelled with different zones are analysed by means of their structure andcase, theirthematic mutual interactions. graphs can help identifycolors issuesand andcolor characteristics analysed by means of their structure and their mutual interactions. Contracted graphs can help identify issues and characteristics of the original structures that were not visible before. of This the original structures and thatdiscusses were not visible before. paper introduces the problem of contracting possibly large colored graphs into much smaller representatives. Thisprovides paper introduces and discusses the problem of contracting graphs into much representatives. It also a novel serial but parallelizable algorithm to tackle possibly this task.large Somecolored initial performance plots smaller are given and discussed It also provides a novel serial but parallelizable algorithm to tackle this task. Some initial performance plots are given and discussed together with hints for future development. together with hints for future development.
2022
Istituto Applicazioni del Calcolo ''Mauro Picone''
Graph Contraction
Clustering Contraction/Analysis
Divide-et-impera
Graph Analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/414115
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