Parallel Computing Aspects in Improved Edge Cover Based Graph Coloring Algorithm Article Swipe
YOU?
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· 2017
· Open Access
·
· DOI: https://doi.org/10.17485/ijst/2017/v10i25/115117
Objective: To improve the Edge Cover based Graph Coloring Algorithm (ECGCA) using independent set by incorporating parallel computing aspects in algorithm. Finding optimum time complexity is one of the main objectives of this paper. Methods/Statistical Analysis: This paper introduced some modification in ECGCA. Algorithm is implemented and tested using Java programming language. Java multithreading concept is used to achieve parallel computing in algorithm. DIMACS graph instances are used to test algorithm. Finding: Algorithm is tested on more than 75 DIMACS graph instances. To analyze the time complexity, execution time of algorithm in seconds is calculated by program. Algorithm is tested on different DIMACS graph instances. Test data is analyze in this paper and found that proposed algorithm executed in optimum time for large graphs. This paper also compared parallel algorithm and found that proposed parallel algorithm is less time complex than sequential algorithm. Most of the exact graph coloring algorithms are not suitable for large graph (more than 100 vertices) but proposed algorithm is tested on many large graphs and high execution success rate of algorithm is achieved. Application: This paper shows the experimental results of different type of application data. It means this algorithm can be used for maximum types of applications. Keywords: Edge Cover, Graph Coloring, Independent Set, Multithreading, Parallel Computing, Vertex Coloring
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17485/ijst/2017/v10i25/115117
- http://www.indjst.org/index.php/indjst/article/download/115117/80823
- OA Status
- diamond
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2734866571
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2734866571Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17485/ijst/2017/v10i25/115117Digital Object Identifier
- Title
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Parallel Computing Aspects in Improved Edge Cover Based Graph Coloring AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-02-01Full publication date if available
- Authors
-
Harish Patidar, Prąsun Chakrabarti, Amrit GhoshList of authors in order
- Landing page
-
https://doi.org/10.17485/ijst/2017/v10i25/115117Publisher landing page
- PDF URL
-
https://www.indjst.org/index.php/indjst/article/download/115117/80823Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.indjst.org/index.php/indjst/article/download/115117/80823Direct OA link when available
- Concepts
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Computer science, Algorithm, Graph coloring, Floyd–Warshall algorithm, Greedy coloring, Edge coloring, Graph, Theoretical computer science, Graph power, Shortest path problem, Dijkstra's algorithm, Line graphTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- References (count)
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13Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.aspects | 18 |
| abstract_inverted_index.complex | 139 |
| abstract_inverted_index.concept | 54 |
| abstract_inverted_index.graphs. | 123 |
| abstract_inverted_index.improve | 2 |
| abstract_inverted_index.maximum | 199 |
| abstract_inverted_index.optimum | 22, 119 |
| abstract_inverted_index.results | 184 |
| abstract_inverted_index.seconds | 92 |
| abstract_inverted_index.success | 172 |
| abstract_inverted_index.Coloring | 8, 214 |
| abstract_inverted_index.Finding: | 71 |
| abstract_inverted_index.Parallel | 211 |
| abstract_inverted_index.coloring | 148 |
| abstract_inverted_index.compared | 127 |
| abstract_inverted_index.executed | 117 |
| abstract_inverted_index.parallel | 16, 59, 128, 134 |
| abstract_inverted_index.program. | 96 |
| abstract_inverted_index.proposed | 115, 133, 161 |
| abstract_inverted_index.suitable | 152 |
| abstract_inverted_index.Algorithm | 9, 43, 72, 97 |
| abstract_inverted_index.Analysis: | 35 |
| abstract_inverted_index.Coloring, | 207 |
| abstract_inverted_index.Keywords: | 203 |
| abstract_inverted_index.achieved. | 177 |
| abstract_inverted_index.algorithm | 90, 116, 129, 135, 162, 175, 194 |
| abstract_inverted_index.computing | 17, 60 |
| abstract_inverted_index.different | 101, 186 |
| abstract_inverted_index.execution | 87, 171 |
| abstract_inverted_index.instances | 65 |
| abstract_inverted_index.language. | 51 |
| abstract_inverted_index.vertices) | 159 |
| abstract_inverted_index.Computing, | 212 |
| abstract_inverted_index.Objective: | 0 |
| abstract_inverted_index.algorithm. | 20, 62, 70, 142 |
| abstract_inverted_index.algorithms | 149 |
| abstract_inverted_index.calculated | 94 |
| abstract_inverted_index.complexity | 24 |
| abstract_inverted_index.instances. | 81, 104 |
| abstract_inverted_index.introduced | 38 |
| abstract_inverted_index.objectives | 30 |
| abstract_inverted_index.sequential | 141 |
| abstract_inverted_index.Independent | 208 |
| abstract_inverted_index.application | 189 |
| abstract_inverted_index.complexity, | 86 |
| abstract_inverted_index.implemented | 45 |
| abstract_inverted_index.independent | 12 |
| abstract_inverted_index.programming | 50 |
| abstract_inverted_index.Application: | 178 |
| abstract_inverted_index.experimental | 183 |
| abstract_inverted_index.modification | 40 |
| abstract_inverted_index.applications. | 202 |
| abstract_inverted_index.incorporating | 15 |
| abstract_inverted_index.multithreading | 53 |
| abstract_inverted_index.Multithreading, | 210 |
| abstract_inverted_index.Methods/Statistical | 34 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.11292464 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |