Efficient computation of maximum weighted independent sets on weighted dynamic graph
An independent set is a set of vertices in a graph in which no two vertices are adjacent to each other. The maximum weighted independent set is the independent set with the largest sum of weights in a weighted graph. Considering that existing methods are inefficient when computing the maximum weight...
Gespeichert in:
Veröffentlicht in: | The Journal of supercomputing 2024-05, Vol.80 (8), p.10418-10443 |
---|---|
Hauptverfasser: | , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 10443 |
---|---|
container_issue | 8 |
container_start_page | 10418 |
container_title | The Journal of supercomputing |
container_volume | 80 |
creator | Tan, Yuting Zhou, Junfeng Rong, Xinqi Du, Ming Qi, Caiyun |
description | An independent set is a set of vertices in a graph in which no two vertices are adjacent to each other. The maximum weighted independent set is the independent set with the largest sum of weights in a weighted graph. Considering that existing methods are inefficient when computing the maximum weighted independent set, we propose a combined neighbor reduction rule and a loss value-based greedy strategy to improve the efficiency and increase the weight of the independent set. Additionally, we propose the efficient approximate algorithms for the maximum weighted independent set on dynamic graphs. We decompose the weight change problem into five cases and propose corresponding strategies for each case. Finally, we conduct experiments on 8 real-world datasets to verify the effectiveness and efficiency of the proposed methods. |
doi_str_mv | 10.1007/s11227-023-05841-9 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3051510826</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3051510826</sourcerecordid><originalsourceid>FETCH-LOGICAL-c270t-f735993604c0d73d5561336e68b02395477f4d872ac8fa15c5a3fec0268c92b73</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKt_wFPA8-rka5M9SqlWKHip55BmkzbF_TDZRfvvja7ozcsMwzzvvMyL0DWBWwIg7xIhlMoCKCtAKE6K6gTNiJB55IqfohlUFAolOD1HFykdAIAzyWZos_Q-2ODaAduu6cfBDKFrcedxYz5CMzb43YXdfnA1Dm3tepdLZpMbEs7c77I-tqYJFu-i6feX6Myb1-SufvocvTwsN4tVsX5-fFrcrwtLJQyFl0xUFSuBW6glq4UoCWOlK9U2P1IJLqXntZLUWOUNEVYY5p0FWipb0a1kc3Qz3e1j9za6NOhDN8Y2W2oGgggCipaZohNlY5dSdF73MTQmHjUB_ZWentLT2VR_p6erLGKTKGW43bn4d_of1Scy4nIb</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3051510826</pqid></control><display><type>article</type><title>Efficient computation of maximum weighted independent sets on weighted dynamic graph</title><source>SpringerLink Journals</source><creator>Tan, Yuting ; Zhou, Junfeng ; Rong, Xinqi ; Du, Ming ; Qi, Caiyun</creator><creatorcontrib>Tan, Yuting ; Zhou, Junfeng ; Rong, Xinqi ; Du, Ming ; Qi, Caiyun</creatorcontrib><description>An independent set is a set of vertices in a graph in which no two vertices are adjacent to each other. The maximum weighted independent set is the independent set with the largest sum of weights in a weighted graph. Considering that existing methods are inefficient when computing the maximum weighted independent set, we propose a combined neighbor reduction rule and a loss value-based greedy strategy to improve the efficiency and increase the weight of the independent set. Additionally, we propose the efficient approximate algorithms for the maximum weighted independent set on dynamic graphs. We decompose the weight change problem into five cases and propose corresponding strategies for each case. Finally, we conduct experiments on 8 real-world datasets to verify the effectiveness and efficiency of the proposed methods.</description><identifier>ISSN: 0920-8542</identifier><identifier>EISSN: 1573-0484</identifier><identifier>DOI: 10.1007/s11227-023-05841-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Apexes ; Compilers ; Computer Science ; Graph theory ; Interpreters ; Processor Architectures ; Programming Languages</subject><ispartof>The Journal of supercomputing, 2024-05, Vol.80 (8), p.10418-10443</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-f735993604c0d73d5561336e68b02395477f4d872ac8fa15c5a3fec0268c92b73</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11227-023-05841-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11227-023-05841-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Tan, Yuting</creatorcontrib><creatorcontrib>Zhou, Junfeng</creatorcontrib><creatorcontrib>Rong, Xinqi</creatorcontrib><creatorcontrib>Du, Ming</creatorcontrib><creatorcontrib>Qi, Caiyun</creatorcontrib><title>Efficient computation of maximum weighted independent sets on weighted dynamic graph</title><title>The Journal of supercomputing</title><addtitle>J Supercomput</addtitle><description>An independent set is a set of vertices in a graph in which no two vertices are adjacent to each other. The maximum weighted independent set is the independent set with the largest sum of weights in a weighted graph. Considering that existing methods are inefficient when computing the maximum weighted independent set, we propose a combined neighbor reduction rule and a loss value-based greedy strategy to improve the efficiency and increase the weight of the independent set. Additionally, we propose the efficient approximate algorithms for the maximum weighted independent set on dynamic graphs. We decompose the weight change problem into five cases and propose corresponding strategies for each case. Finally, we conduct experiments on 8 real-world datasets to verify the effectiveness and efficiency of the proposed methods.</description><subject>Algorithms</subject><subject>Apexes</subject><subject>Compilers</subject><subject>Computer Science</subject><subject>Graph theory</subject><subject>Interpreters</subject><subject>Processor Architectures</subject><subject>Programming Languages</subject><issn>0920-8542</issn><issn>1573-0484</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKt_wFPA8-rka5M9SqlWKHip55BmkzbF_TDZRfvvja7ozcsMwzzvvMyL0DWBWwIg7xIhlMoCKCtAKE6K6gTNiJB55IqfohlUFAolOD1HFykdAIAzyWZos_Q-2ODaAduu6cfBDKFrcedxYz5CMzb43YXdfnA1Dm3tepdLZpMbEs7c77I-tqYJFu-i6feX6Myb1-SufvocvTwsN4tVsX5-fFrcrwtLJQyFl0xUFSuBW6glq4UoCWOlK9U2P1IJLqXntZLUWOUNEVYY5p0FWipb0a1kc3Qz3e1j9za6NOhDN8Y2W2oGgggCipaZohNlY5dSdF73MTQmHjUB_ZWentLT2VR_p6erLGKTKGW43bn4d_of1Scy4nIb</recordid><startdate>20240501</startdate><enddate>20240501</enddate><creator>Tan, Yuting</creator><creator>Zhou, Junfeng</creator><creator>Rong, Xinqi</creator><creator>Du, Ming</creator><creator>Qi, Caiyun</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20240501</creationdate><title>Efficient computation of maximum weighted independent sets on weighted dynamic graph</title><author>Tan, Yuting ; Zhou, Junfeng ; Rong, Xinqi ; Du, Ming ; Qi, Caiyun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-f735993604c0d73d5561336e68b02395477f4d872ac8fa15c5a3fec0268c92b73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Apexes</topic><topic>Compilers</topic><topic>Computer Science</topic><topic>Graph theory</topic><topic>Interpreters</topic><topic>Processor Architectures</topic><topic>Programming Languages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tan, Yuting</creatorcontrib><creatorcontrib>Zhou, Junfeng</creatorcontrib><creatorcontrib>Rong, Xinqi</creatorcontrib><creatorcontrib>Du, Ming</creatorcontrib><creatorcontrib>Qi, Caiyun</creatorcontrib><collection>CrossRef</collection><jtitle>The Journal of supercomputing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tan, Yuting</au><au>Zhou, Junfeng</au><au>Rong, Xinqi</au><au>Du, Ming</au><au>Qi, Caiyun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient computation of maximum weighted independent sets on weighted dynamic graph</atitle><jtitle>The Journal of supercomputing</jtitle><stitle>J Supercomput</stitle><date>2024-05-01</date><risdate>2024</risdate><volume>80</volume><issue>8</issue><spage>10418</spage><epage>10443</epage><pages>10418-10443</pages><issn>0920-8542</issn><eissn>1573-0484</eissn><abstract>An independent set is a set of vertices in a graph in which no two vertices are adjacent to each other. The maximum weighted independent set is the independent set with the largest sum of weights in a weighted graph. Considering that existing methods are inefficient when computing the maximum weighted independent set, we propose a combined neighbor reduction rule and a loss value-based greedy strategy to improve the efficiency and increase the weight of the independent set. Additionally, we propose the efficient approximate algorithms for the maximum weighted independent set on dynamic graphs. We decompose the weight change problem into five cases and propose corresponding strategies for each case. Finally, we conduct experiments on 8 real-world datasets to verify the effectiveness and efficiency of the proposed methods.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11227-023-05841-9</doi><tpages>26</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0920-8542 |
ispartof | The Journal of supercomputing, 2024-05, Vol.80 (8), p.10418-10443 |
issn | 0920-8542 1573-0484 |
language | eng |
recordid | cdi_proquest_journals_3051510826 |
source | SpringerLink Journals |
subjects | Algorithms Apexes Compilers Computer Science Graph theory Interpreters Processor Architectures Programming Languages |
title | Efficient computation of maximum weighted independent sets on weighted dynamic graph |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T05%3A59%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Efficient%20computation%20of%20maximum%20weighted%20independent%20sets%20on%20weighted%20dynamic%20graph&rft.jtitle=The%20Journal%20of%20supercomputing&rft.au=Tan,%20Yuting&rft.date=2024-05-01&rft.volume=80&rft.issue=8&rft.spage=10418&rft.epage=10443&rft.pages=10418-10443&rft.issn=0920-8542&rft.eissn=1573-0484&rft_id=info:doi/10.1007/s11227-023-05841-9&rft_dat=%3Cproquest_cross%3E3051510826%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3051510826&rft_id=info:pmid/&rfr_iscdi=true |