MatchSim: a novel similarity measure based on maximum neighborhood matching

Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the int...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lin, Zhenjiang [verfasserIn] Lyu, Michael R. [verfasserIn] King, Irwin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Link-based similarity measure Link analysis Web mining Maximum matching

Übergeordnetes Werk:	Enthalten in: Knowledge and information systems - London : Springer, 1999, 32(2011), 1 vom: 14. Juni, Seite 141-166
Übergeordnetes Werk:	volume:32 ; year:2011 ; number:1 ; day:14 ; month:06 ; pages:141-166

Links:	Volltext

DOI / URN:	10.1007/s10115-011-0427-z

Katalog-ID:	SPR009121870

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520			\|a Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy.
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700	1		\|a King, Irwin \|e verfasserin \|4 aut
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allfields	10.1007/s10115-011-0427-z doi (DE-627)SPR009121870 (SPR)s10115-011-0427-z-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Lin, Zhenjiang verfasserin aut MatchSim: a novel similarity measure based on maximum neighborhood matching 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy. Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213 Lyu, Michael R. verfasserin aut King, Irwin verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 32(2011), 1 vom: 14. Juni, Seite 141-166 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:32 year:2011 number:1 day:14 month:06 pages:141-166 https://dx.doi.org/10.1007/s10115-011-0427-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 ASE 54.64 ASE AR 32 2011 1 14 06 141-166
spelling	10.1007/s10115-011-0427-z doi (DE-627)SPR009121870 (SPR)s10115-011-0427-z-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Lin, Zhenjiang verfasserin aut MatchSim: a novel similarity measure based on maximum neighborhood matching 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy. Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213 Lyu, Michael R. verfasserin aut King, Irwin verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 32(2011), 1 vom: 14. Juni, Seite 141-166 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:32 year:2011 number:1 day:14 month:06 pages:141-166 https://dx.doi.org/10.1007/s10115-011-0427-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 ASE 54.64 ASE AR 32 2011 1 14 06 141-166
allfields_unstemmed	10.1007/s10115-011-0427-z doi (DE-627)SPR009121870 (SPR)s10115-011-0427-z-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Lin, Zhenjiang verfasserin aut MatchSim: a novel similarity measure based on maximum neighborhood matching 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy. Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213 Lyu, Michael R. verfasserin aut King, Irwin verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 32(2011), 1 vom: 14. Juni, Seite 141-166 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:32 year:2011 number:1 day:14 month:06 pages:141-166 https://dx.doi.org/10.1007/s10115-011-0427-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 ASE 54.64 ASE AR 32 2011 1 14 06 141-166
allfieldsGer	10.1007/s10115-011-0427-z doi (DE-627)SPR009121870 (SPR)s10115-011-0427-z-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Lin, Zhenjiang verfasserin aut MatchSim: a novel similarity measure based on maximum neighborhood matching 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy. Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213 Lyu, Michael R. verfasserin aut King, Irwin verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 32(2011), 1 vom: 14. Juni, Seite 141-166 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:32 year:2011 number:1 day:14 month:06 pages:141-166 https://dx.doi.org/10.1007/s10115-011-0427-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 ASE 54.64 ASE AR 32 2011 1 14 06 141-166
allfieldsSound	10.1007/s10115-011-0427-z doi (DE-627)SPR009121870 (SPR)s10115-011-0427-z-e DE-627 ger DE-627 rakwb eng 004 ASE 004 070 ASE 06.74 bkl 54.64 bkl Lin, Zhenjiang verfasserin aut MatchSim: a novel similarity measure based on maximum neighborhood matching 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy. Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213 Lyu, Michael R. verfasserin aut King, Irwin verfasserin aut Enthalten in Knowledge and information systems London : Springer, 1999 32(2011), 1 vom: 14. Juni, Seite 141-166 (DE-627)320627845 (DE-600)2023541-0 0219-3116 nnns volume:32 year:2011 number:1 day:14 month:06 pages:141-166 https://dx.doi.org/10.1007/s10115-011-0427-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 06.74 ASE 54.64 ASE AR 32 2011 1 14 06 141-166
language	English
source	Enthalten in Knowledge and information systems 32(2011), 1 vom: 14. Juni, Seite 141-166 volume:32 year:2011 number:1 day:14 month:06 pages:141-166
sourceStr	Enthalten in Knowledge and information systems 32(2011), 1 vom: 14. Juni, Seite 141-166 volume:32 year:2011 number:1 day:14 month:06 pages:141-166
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Link-based similarity measure Link analysis Web mining Maximum matching
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container_title	Knowledge and information systems
authorswithroles_txt_mv	Lin, Zhenjiang @@aut@@ Lyu, Michael R. @@aut@@ King, Irwin @@aut@@
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spellingShingle	Lin, Zhenjiang ddc 004 bkl 06.74 bkl 54.64 misc Link-based similarity measure misc Link analysis misc Web mining misc Maximum matching MatchSim: a novel similarity measure based on maximum neighborhood matching
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topic_title	004 ASE 004 070 ASE 06.74 bkl 54.64 bkl MatchSim: a novel similarity measure based on maximum neighborhood matching Link-based similarity measure (dpeaa)DE-He213 Link analysis (dpeaa)DE-He213 Web mining (dpeaa)DE-He213 Maximum matching (dpeaa)DE-He213
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title	MatchSim: a novel similarity measure based on maximum neighborhood matching
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title_full	MatchSim: a novel similarity measure based on maximum neighborhood matching
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title_sort	matchsim: a novel similarity measure based on maximum neighborhood matching
title_auth	MatchSim: a novel similarity measure based on maximum neighborhood matching
abstract	Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy.
abstractGer	Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy.
abstract_unstemmed	Abstract Measuring object similarity in a graph is a fundamental data- mining problem in various application domains, including Web linkage mining, social network analysis, information retrieval, and recommender systems. In this paper, we focus on the neighbor-based approach that is based on the intuition that “similar objects have similar neighbors” and propose a novel similarity measure called MatchSim. Our method recursively defines the similarity between two objects by the average similarity of the maximum-matched similar neighbor pairs between them. We show that MatchSim conforms to the basic intuition of similarity; therefore, it can overcome the counterintuitive contradiction in SimRank. Moreover, MatchSim can be viewed as an extension of the traditional neighbor-counting scheme by taking the similarities between neighbors into account, leading to higher flexibility. We present the MatchSim score computation process and prove its convergence. We also analyze its time and space complexity and suggest two accelerating techniques: (1) proposing a simple pruning strategy and (2) adopting an approximation algorithm for maximum matching computation. Experimental results on real-world datasets show that although our method is less efficient computationally, it outperforms classic methods in terms of accuracy.
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title_short	MatchSim: a novel similarity measure based on maximum neighborhood matching
url	https://dx.doi.org/10.1007/s10115-011-0427-z
remote_bool	true
author2	Lyu, Michael R. King, Irwin
author2Str	Lyu, Michael R. King, Irwin
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doi_str	10.1007/s10115-011-0427-z
up_date	2024-07-04T00:45:38.459Z
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