Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate

Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about...
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Autor*in:	Rodrigues, Marcos Wander [verfasserIn] Song, Mark A. Junho [verfasserIn] Zárate, Luis Enrique [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Scientific collaboration network Continuous and dichotomous attributes Knowledge extraction Attributes reduction

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Social network analysis and mining - Wien : Springer, 2011, 11(2021), 1 vom: 04. Aug.
Übergeordnetes Werk:	volume:11 ; year:2021 ; number:1 ; day:04 ; month:08

Links:	Volltext

DOI / URN:	10.1007/s13278-021-00781-9

Katalog-ID:	SPR044736851

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520			\|a Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers.
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allfields	10.1007/s13278-021-00781-9 doi (DE-627)SPR044736851 (SPR)s13278-021-00781-9-e DE-627 ger DE-627 rakwb eng 004 ASE Rodrigues, Marcos Wander verfasserin aut Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213 Song, Mark A. Junho verfasserin aut Zárate, Luis Enrique verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 11(2021), 1 vom: 04. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:11 year:2021 number:1 day:04 month:08 https://dx.doi.org/10.1007/s13278-021-00781-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2021 1 04 08
spelling	10.1007/s13278-021-00781-9 doi (DE-627)SPR044736851 (SPR)s13278-021-00781-9-e DE-627 ger DE-627 rakwb eng 004 ASE Rodrigues, Marcos Wander verfasserin aut Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213 Song, Mark A. Junho verfasserin aut Zárate, Luis Enrique verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 11(2021), 1 vom: 04. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:11 year:2021 number:1 day:04 month:08 https://dx.doi.org/10.1007/s13278-021-00781-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2021 1 04 08
allfields_unstemmed	10.1007/s13278-021-00781-9 doi (DE-627)SPR044736851 (SPR)s13278-021-00781-9-e DE-627 ger DE-627 rakwb eng 004 ASE Rodrigues, Marcos Wander verfasserin aut Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213 Song, Mark A. Junho verfasserin aut Zárate, Luis Enrique verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 11(2021), 1 vom: 04. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:11 year:2021 number:1 day:04 month:08 https://dx.doi.org/10.1007/s13278-021-00781-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2021 1 04 08
allfieldsGer	10.1007/s13278-021-00781-9 doi (DE-627)SPR044736851 (SPR)s13278-021-00781-9-e DE-627 ger DE-627 rakwb eng 004 ASE Rodrigues, Marcos Wander verfasserin aut Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213 Song, Mark A. Junho verfasserin aut Zárate, Luis Enrique verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 11(2021), 1 vom: 04. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:11 year:2021 number:1 day:04 month:08 https://dx.doi.org/10.1007/s13278-021-00781-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2021 1 04 08
allfieldsSound	10.1007/s13278-021-00781-9 doi (DE-627)SPR044736851 (SPR)s13278-021-00781-9-e DE-627 ger DE-627 rakwb eng 004 ASE Rodrigues, Marcos Wander verfasserin aut Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213 Song, Mark A. Junho verfasserin aut Zárate, Luis Enrique verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 11(2021), 1 vom: 04. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:11 year:2021 number:1 day:04 month:08 https://dx.doi.org/10.1007/s13278-021-00781-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_65 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_4012 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2021 1 04 08
language	English
source	Enthalten in Social network analysis and mining 11(2021), 1 vom: 04. Aug. volume:11 year:2021 number:1 day:04 month:08
sourceStr	Enthalten in Social network analysis and mining 11(2021), 1 vom: 04. Aug. volume:11 year:2021 number:1 day:04 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Scientific collaboration network Continuous and dichotomous attributes Knowledge extraction Attributes reduction
dewey-raw	004
isfreeaccess_bool	false
container_title	Social network analysis and mining
authorswithroles_txt_mv	Rodrigues, Marcos Wander @@aut@@ Song, Mark A. Junho @@aut@@ Zárate, Luis Enrique @@aut@@
publishDateDaySort_date	2021-08-04T00:00:00Z
hierarchy_top_id	647305739
dewey-sort	14
id	SPR044736851
language_de	englisch
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author	Rodrigues, Marcos Wander
spellingShingle	Rodrigues, Marcos Wander ddc 004 misc Scientific collaboration network misc Continuous and dichotomous attributes misc Knowledge extraction misc Attributes reduction Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate
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topic_title	004 ASE Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate Scientific collaboration network (dpeaa)DE-He213 Continuous and dichotomous attributes (dpeaa)DE-He213 Knowledge extraction (dpeaa)DE-He213 Attributes reduction (dpeaa)DE-He213
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title	Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate
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title_full	Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate
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author_browse	Rodrigues, Marcos Wander Song, Mark A. Junho Zárate, Luis Enrique
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title_sort	effectively clustering researchers in scientific collaboration networks: case study on researchgate
title_auth	Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate
abstract	Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021
abstractGer	Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021
abstract_unstemmed	Abstract Social networks play a significant role in sharing knowledge. Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. In our dimensionality reduction, we used Factor Analysis, as it preserves the relationships between the variables. In this study, we characterize the profiles of ResearchGate users after applying dimensionality reduction to two different datasets. A dataset of continuous attributes composed of profile metrics and a dataset of dichotomous attributes contained interest topics. We evaluated our methodology using two recommendation applications: (1) Identifying groups of researchers through a global profile extraction process; and (2) Identifying profiles similar to a reference profile. For both applications, we used hierarchical clustering techniques to identify the groups of user profiles. Our experiments show that the Factor Analysis transformation was able to preserve the relevant information in the data, resulting in an effective clustering process for the recommendation system for collaborative networks of researchers. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021
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title_short	Effectively clustering researchers in scientific collaboration networks: case study on ResearchGate
url	https://dx.doi.org/10.1007/s13278-021-00781-9
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author2	Song, Mark A. Junho Zárate, Luis Enrique
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up_date	2024-07-04T02:07:41.202Z
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Scientific collaboration online networks allow scientific articles and research results to be shared, and the interaction and possible collaboration between researchers. These networks have many users and store varied data about each of them, and which of the data are used to characterize and grouping similar users. The number of attributes available about each instance (user) can reach several hundred, making this a problem with high dimensionality. Thus, dimensionality reduction is indispensable to remove redundant and irrelevant attributes to improve machine learning algorithms’ performance and make models more understandable. In order to produce an efficient recommendation system for collaborative research, one of the main challenges of dimensionality reduction techniques is guaranteeing that the information of the data is represented in the reduced dataset after the reduction. 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