Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor
Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study fa...
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Veröffentlicht in: | Chaos (Woodbury, N.Y.) N.Y.), 2020-01, Vol.30 (1), p.013104-013104 |
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creator | Li, Shibao Huang, Junwei Liu, Jianhang Huang, Tingpei Chen, Haihua |
description | Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness. |
doi_str_mv | 10.1063/1.5094448 |
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An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness.</description><identifier>ISSN: 1054-1500</identifier><identifier>EISSN: 1089-7682</identifier><identifier>DOI: 10.1063/1.5094448</identifier><identifier>PMID: 32013467</identifier><identifier>CODEN: CHAOEH</identifier><language>eng</language><publisher>United States: American Institute of Physics</publisher><subject>Algorithms ; Evolutionary algorithms ; Mathematical analysis ; Networks ; Nodes ; Similarity</subject><ispartof>Chaos (Woodbury, N.Y.), 2020-01, Vol.30 (1), p.013104-013104</ispartof><rights>Author(s)</rights><rights>2020 Author(s). 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Experimental results on six disparate real networks demonstrate that the relative-path-based method can obtain greater prediction accuracy than other methods, as well as performance robustness.</description><subject>Algorithms</subject><subject>Evolutionary algorithms</subject><subject>Mathematical analysis</subject><subject>Networks</subject><subject>Nodes</subject><subject>Similarity</subject><issn>1054-1500</issn><issn>1089-7682</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp90F1LHDEUBuAglq61vfAPSMAbFUbPSTJflyK1FhYKpV4PmSSzxp2ZjElG679vlt0qtCAEkosnb05eQo4QLhAKfokXOdRCiGqPHCBUdVYWFdvfnHORYQ6wIJ9CeAAAZDz_SBacAXJRlAfE_DS9jPbJZJOM91krg9FU9ivnbbwfaOc87e24ppM32qpo3UjTUm6YevObjiY-O78OdA52XFFJ032raLCD7WVKeKGdVNH5z-RDJ_tgvuz2Q3J38_XX9W22_PHt-_XVMlO84jFrC8QK0OhKlXVZdK0RUmDJUXHVFkrnsmxRKKGYKtAIzZjQZasA8rZgrNT8kJxucyfvHmcTYjPYoEzfy9G4OTTp91BDXmOe6Mk_9MHNfkzTJcUZg5pzkdTZVinvQvCmayZvB-lfGoRm032Dza77ZI93iXM7GP0q_5adwPkWBGWj3HT5ap6cf0tqJt29h_9_-g_q5pqX</recordid><startdate>202001</startdate><enddate>202001</enddate><creator>Li, Shibao</creator><creator>Huang, Junwei</creator><creator>Liu, Jianhang</creator><creator>Huang, Tingpei</creator><creator>Chen, Haihua</creator><general>American Institute of Physics</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope><scope>7X8</scope></search><sort><creationdate>202001</creationdate><title>Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor</title><author>Li, Shibao ; Huang, Junwei ; Liu, Jianhang ; Huang, Tingpei ; Chen, Haihua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c383t-b611801ed8c7976fbe4a41731c3cb6cd5a7b14c4c2c61e4d224d7bc005b6227d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Evolutionary algorithms</topic><topic>Mathematical analysis</topic><topic>Networks</topic><topic>Nodes</topic><topic>Similarity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Shibao</creatorcontrib><creatorcontrib>Huang, Junwei</creatorcontrib><creatorcontrib>Liu, Jianhang</creatorcontrib><creatorcontrib>Huang, Tingpei</creatorcontrib><creatorcontrib>Chen, Haihua</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Chaos (Woodbury, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Shibao</au><au>Huang, Junwei</au><au>Liu, Jianhang</au><au>Huang, Tingpei</au><au>Chen, Haihua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor</atitle><jtitle>Chaos (Woodbury, N.Y.)</jtitle><addtitle>Chaos</addtitle><date>2020-01</date><risdate>2020</risdate><volume>30</volume><issue>1</issue><spage>013104</spage><epage>013104</epage><pages>013104-013104</pages><issn>1054-1500</issn><eissn>1089-7682</eissn><coden>CHAOEH</coden><abstract>Complex networks have found many applications in various fields. An important problem in theories of complex networks is to find factors that aid link prediction, which is needed for network reconstruction and to study network evolution mechanisms. Though current similarity-based algorithms study factors of common neighbors and local paths connecting a target node pair, they ignore factor information on paths between a node and its neighbors. Therefore, this paper first supposes that paths between nodes and neighbors provide basic similarity features. Accordingly, we propose a so-called relative-path-based method. This method utilizes factor information on paths between nodes and neighbors, besides paths between node pairs, in similarity calculation for link prediction. Furthermore, we solve the problem of determining the parameters in our algorithm as well as in other algorithms after a series of discoveries and validations. 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subjects | Algorithms Evolutionary algorithms Mathematical analysis Networks Nodes Similarity |
title | Relative-path-based algorithm for link prediction on complex networks using a basic similarity factor |
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