Graph drawing using Jaya
Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performa...
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| description | Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm's performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm's effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős-Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time. |
| doi_str_mv | 10.1371/journal.pone.0287744 |
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In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm's performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm's effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős-Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0287744</identifier><identifier>PMID: 37368896</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Aesthetics ; Algorithms ; Analysis ; Animals ; Biology and Life Sciences ; Climbing ; Computer and Information Sciences ; Data Visualization ; Datasets ; Efficiency ; Esthetics ; Graph theory ; Graphic methods ; Graphs ; Holometabola ; Humans ; Hypercubes ; Latin hypercube sampling ; Layouts ; Methods ; Nodes ; Objective function ; Optimization ; Optimization techniques ; Parameters ; Physical Sciences ; Population number ; Population sampling ; Research and Analysis Methods ; Sampling ; Scientific visualization ; Search algorithms ; Search methods ; Simulated annealing ; Straight lines ; Synthetic data ; Visualization ; Visualization (Computers)</subject><ispartof>PloS one, 2023-06, Vol.18 (6), p.e0287744-e0287744</ispartof><rights>Copyright: © 2023 Dib, Rodgers. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Dib, Rodgers. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Dib, Rodgers 2023 Dib, Rodgers</rights><rights>2023 Dib, Rodgers. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dib, Fadi K</au><au>Rodgers, Peter</au><au>Donta, Praveen Kumar</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Graph drawing using Jaya</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-06-27</date><risdate>2023</risdate><volume>18</volume><issue>6</issue><spage>e0287744</spage><epage>e0287744</epage><pages>e0287744-e0287744</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm's performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm's effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős-Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37368896</pmid><doi>10.1371/journal.pone.0287744</doi><tpages>e0287744</tpages><orcidid>https://orcid.org/0000-0003-2689-3305</orcidid><orcidid>https://orcid.org/0000-0002-4100-3596</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aesthetics Algorithms Analysis Animals Biology and Life Sciences Climbing Computer and Information Sciences Data Visualization Datasets Efficiency Esthetics Graph theory Graphic methods Graphs Holometabola Humans Hypercubes Latin hypercube sampling Layouts Methods Nodes Objective function Optimization Optimization techniques Parameters Physical Sciences Population number Population sampling Research and Analysis Methods Sampling Scientific visualization Search algorithms Search methods Simulated annealing Straight lines Synthetic data Visualization Visualization (Computers) |
| title | Graph drawing using Jaya |
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