Probabilistic graphical models in modern social network analysis

Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace wi...
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Autor*in:	Farasat, Alireza [verfasserIn] Nikolaev, Alexander Srihari, Sargur N. Blair, Rachael Hageman

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Probabilistic graphical modeling Social network analysis Bayesian networks Markov networks Exponential random graph models Markov logic networks Social influence Network sampling

Anmerkung:	© Springer-Verlag Wien 2015

Übergeordnetes Werk:	Enthalten in: Social network analysis and mining - Wien : Springer, 2011, 5(2015), 1 vom: 19. Okt.
Übergeordnetes Werk:	volume:5 ; year:2015 ; number:1 ; day:19 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s13278-015-0289-6

Katalog-ID:	SPR031183581

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912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_171
912			\|a GBV_ILN_187
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
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912			\|a GBV_ILN_285
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allfields	10.1007/s13278-015-0289-6 doi (DE-627)SPR031183581 (SPR)s13278-015-0289-6-e DE-627 ger DE-627 rakwb eng Farasat, Alireza verfasserin aut Probabilistic graphical models in modern social network analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213 Nikolaev, Alexander aut Srihari, Sargur N. aut Blair, Rachael Hageman aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 19. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:19 month:10 https://dx.doi.org/10.1007/s13278-015-0289-6 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 5 2015 1 19 10
spelling	10.1007/s13278-015-0289-6 doi (DE-627)SPR031183581 (SPR)s13278-015-0289-6-e DE-627 ger DE-627 rakwb eng Farasat, Alireza verfasserin aut Probabilistic graphical models in modern social network analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213 Nikolaev, Alexander aut Srihari, Sargur N. aut Blair, Rachael Hageman aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 19. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:19 month:10 https://dx.doi.org/10.1007/s13278-015-0289-6 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 5 2015 1 19 10
allfields_unstemmed	10.1007/s13278-015-0289-6 doi (DE-627)SPR031183581 (SPR)s13278-015-0289-6-e DE-627 ger DE-627 rakwb eng Farasat, Alireza verfasserin aut Probabilistic graphical models in modern social network analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213 Nikolaev, Alexander aut Srihari, Sargur N. aut Blair, Rachael Hageman aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 19. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:19 month:10 https://dx.doi.org/10.1007/s13278-015-0289-6 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 5 2015 1 19 10
allfieldsGer	10.1007/s13278-015-0289-6 doi (DE-627)SPR031183581 (SPR)s13278-015-0289-6-e DE-627 ger DE-627 rakwb eng Farasat, Alireza verfasserin aut Probabilistic graphical models in modern social network analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213 Nikolaev, Alexander aut Srihari, Sargur N. aut Blair, Rachael Hageman aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 19. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:19 month:10 https://dx.doi.org/10.1007/s13278-015-0289-6 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 5 2015 1 19 10
allfieldsSound	10.1007/s13278-015-0289-6 doi (DE-627)SPR031183581 (SPR)s13278-015-0289-6-e DE-627 ger DE-627 rakwb eng Farasat, Alireza verfasserin aut Probabilistic graphical models in modern social network analysis 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Wien 2015 Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213 Nikolaev, Alexander aut Srihari, Sargur N. aut Blair, Rachael Hageman aut Enthalten in Social network analysis and mining Wien : Springer, 2011 5(2015), 1 vom: 19. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:5 year:2015 number:1 day:19 month:10 https://dx.doi.org/10.1007/s13278-015-0289-6 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_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_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_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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 AR 5 2015 1 19 10
language	English
source	Enthalten in Social network analysis and mining 5(2015), 1 vom: 19. Okt. volume:5 year:2015 number:1 day:19 month:10
sourceStr	Enthalten in Social network analysis and mining 5(2015), 1 vom: 19. Okt. volume:5 year:2015 number:1 day:19 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Probabilistic graphical modeling Social network analysis Bayesian networks Markov networks Exponential random graph models Markov logic networks Social influence Network sampling
isfreeaccess_bool	false
container_title	Social network analysis and mining
authorswithroles_txt_mv	Farasat, Alireza @@aut@@ Nikolaev, Alexander @@aut@@ Srihari, Sargur N. @@aut@@ Blair, Rachael Hageman @@aut@@
publishDateDaySort_date	2015-10-19T00:00:00Z
hierarchy_top_id	647305739
id	SPR031183581
language_de	englisch
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author	Farasat, Alireza
spellingShingle	Farasat, Alireza misc Probabilistic graphical modeling misc Social network analysis misc Bayesian networks misc Markov networks misc Exponential random graph models misc Markov logic networks misc Social influence misc Network sampling Probabilistic graphical models in modern social network analysis
authorStr	Farasat, Alireza
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topic_title	Probabilistic graphical models in modern social network analysis Probabilistic graphical modeling (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Bayesian networks (dpeaa)DE-He213 Markov networks (dpeaa)DE-He213 Exponential random graph models (dpeaa)DE-He213 Markov logic networks (dpeaa)DE-He213 Social influence (dpeaa)DE-He213 Network sampling (dpeaa)DE-He213
topic	misc Probabilistic graphical modeling misc Social network analysis misc Bayesian networks misc Markov networks misc Exponential random graph models misc Markov logic networks misc Social influence misc Network sampling
topic_unstemmed	misc Probabilistic graphical modeling misc Social network analysis misc Bayesian networks misc Markov networks misc Exponential random graph models misc Markov logic networks misc Social influence misc Network sampling
topic_browse	misc Probabilistic graphical modeling misc Social network analysis misc Bayesian networks misc Markov networks misc Exponential random graph models misc Markov logic networks misc Social influence misc Network sampling
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title	Probabilistic graphical models in modern social network analysis
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title_full	Probabilistic graphical models in modern social network analysis
author_sort	Farasat, Alireza
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author_browse	Farasat, Alireza Nikolaev, Alexander Srihari, Sargur N. Blair, Rachael Hageman
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doi_str_mv	10.1007/s13278-015-0289-6
title_sort	probabilistic graphical models in modern social network analysis
title_auth	Probabilistic graphical models in modern social network analysis
abstract	Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. © Springer-Verlag Wien 2015
abstractGer	Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. © Springer-Verlag Wien 2015
abstract_unstemmed	Abstract The advent and availability of technology has brought us closer than ever through social networks. Consequently, there is a growing emphasis on mining social networks to extract information for knowledge and discovery. However, methods for social network analysis (SNA) have not kept pace with the data explosion. In this review, we describe directed and undirected probabilistic graphical models (PGMs), and highlight recent applications to social networks. PGMs represent a flexible class of models that can be adapted to address many of the current challenges in SNA. In this work, we motivate their use with simple and accessible examples to demonstrate the modeling and connect to theory. In addition, recent applications in modern SNA are highlighted, including the estimation and quantification of importance, propagation of influence, trust (and distrust), link and profile prediction, privacy protection, and news spread through microblogging. Applications are selected to demonstrate the flexibility and predictive capabilities of PGMs in SNA. Finally, we conclude with a discussion of challenges and opportunities for PGMs in social networks. © Springer-Verlag Wien 2015
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title_short	Probabilistic graphical models in modern social network analysis
url	https://dx.doi.org/10.1007/s13278-015-0289-6
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author2	Nikolaev, Alexander Srihari, Sargur N. Blair, Rachael Hageman
author2Str	Nikolaev, Alexander Srihari, Sargur N. Blair, Rachael Hageman
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doi_str	10.1007/s13278-015-0289-6
up_date	2024-07-03T22:28:26.308Z
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Probabilistic graphical models in modern social network analysis

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