Online social network trend discovery using frequent subgraph mining

Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (F...
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Gespeichert in:

Autor*in:	Rehman, Saif Ur [verfasserIn] Asghar, Sohail [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Graph mining Frequent pattern trends Frequent subgraph mining Social network Social network analysis

Übergeordnetes Werk:	Enthalten in: Social network analysis and mining - Wien : Springer, 2011, 10(2020), 1 vom: 11. Aug.
Übergeordnetes Werk:	volume:10 ; year:2020 ; number:1 ; day:11 ; month:08

Links:	Volltext

DOI / URN:	10.1007/s13278-020-00682-3

Katalog-ID:	SPR040624145

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520			\|a Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.
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publishDate	2020
allfields	10.1007/s13278-020-00682-3 doi (DE-627)SPR040624145 (DE-599)SPRs13278-020-00682-3-e (SPR)s13278-020-00682-3-e DE-627 ger DE-627 rakwb eng 004 ASE Rehman, Saif Ur verfasserin aut Online social network trend discovery using frequent subgraph mining 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth. Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Asghar, Sohail verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 10(2020), 1 vom: 11. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:10 year:2020 number:1 day:11 month:08 https://dx.doi.org/10.1007/s13278-020-00682-3 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 10 2020 1 11 08
spelling	10.1007/s13278-020-00682-3 doi (DE-627)SPR040624145 (DE-599)SPRs13278-020-00682-3-e (SPR)s13278-020-00682-3-e DE-627 ger DE-627 rakwb eng 004 ASE Rehman, Saif Ur verfasserin aut Online social network trend discovery using frequent subgraph mining 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth. Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Asghar, Sohail verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 10(2020), 1 vom: 11. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:10 year:2020 number:1 day:11 month:08 https://dx.doi.org/10.1007/s13278-020-00682-3 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 10 2020 1 11 08
allfields_unstemmed	10.1007/s13278-020-00682-3 doi (DE-627)SPR040624145 (DE-599)SPRs13278-020-00682-3-e (SPR)s13278-020-00682-3-e DE-627 ger DE-627 rakwb eng 004 ASE Rehman, Saif Ur verfasserin aut Online social network trend discovery using frequent subgraph mining 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth. Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Asghar, Sohail verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 10(2020), 1 vom: 11. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:10 year:2020 number:1 day:11 month:08 https://dx.doi.org/10.1007/s13278-020-00682-3 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 10 2020 1 11 08
allfieldsGer	10.1007/s13278-020-00682-3 doi (DE-627)SPR040624145 (DE-599)SPRs13278-020-00682-3-e (SPR)s13278-020-00682-3-e DE-627 ger DE-627 rakwb eng 004 ASE Rehman, Saif Ur verfasserin aut Online social network trend discovery using frequent subgraph mining 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth. Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Asghar, Sohail verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 10(2020), 1 vom: 11. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:10 year:2020 number:1 day:11 month:08 https://dx.doi.org/10.1007/s13278-020-00682-3 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 10 2020 1 11 08
allfieldsSound	10.1007/s13278-020-00682-3 doi (DE-627)SPR040624145 (DE-599)SPRs13278-020-00682-3-e (SPR)s13278-020-00682-3-e DE-627 ger DE-627 rakwb eng 004 ASE Rehman, Saif Ur verfasserin aut Online social network trend discovery using frequent subgraph mining 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth. Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213 Asghar, Sohail verfasserin aut Enthalten in Social network analysis and mining Wien : Springer, 2011 10(2020), 1 vom: 11. Aug. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:10 year:2020 number:1 day:11 month:08 https://dx.doi.org/10.1007/s13278-020-00682-3 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 10 2020 1 11 08
language	English
source	Enthalten in Social network analysis and mining 10(2020), 1 vom: 11. Aug. volume:10 year:2020 number:1 day:11 month:08
sourceStr	Enthalten in Social network analysis and mining 10(2020), 1 vom: 11. Aug. volume:10 year:2020 number:1 day:11 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Graph mining Frequent pattern trends Frequent subgraph mining Social network Social network analysis
dewey-raw	004
isfreeaccess_bool	false
container_title	Social network analysis and mining
authorswithroles_txt_mv	Rehman, Saif Ur @@aut@@ Asghar, Sohail @@aut@@
publishDateDaySort_date	2020-08-11T00:00:00Z
hierarchy_top_id	647305739
dewey-sort	14
id	SPR040624145
language_de	englisch
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author	Rehman, Saif Ur
spellingShingle	Rehman, Saif Ur ddc 004 misc Graph mining misc Frequent pattern trends misc Frequent subgraph mining misc Social network misc Social network analysis Online social network trend discovery using frequent subgraph mining
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topic_title	004 ASE Online social network trend discovery using frequent subgraph mining Graph mining (dpeaa)DE-He213 Frequent pattern trends (dpeaa)DE-He213 Frequent subgraph mining (dpeaa)DE-He213 Social network (dpeaa)DE-He213 Social network analysis (dpeaa)DE-He213
topic	ddc 004 misc Graph mining misc Frequent pattern trends misc Frequent subgraph mining misc Social network misc Social network analysis
topic_unstemmed	ddc 004 misc Graph mining misc Frequent pattern trends misc Frequent subgraph mining misc Social network misc Social network analysis
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title	Online social network trend discovery using frequent subgraph mining
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title_full	Online social network trend discovery using frequent subgraph mining
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author_browse	Rehman, Saif Ur Asghar, Sohail
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title_sort	online social network trend discovery using frequent subgraph mining
title_auth	Online social network trend discovery using frequent subgraph mining
abstract	Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.
abstractGer	Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.
abstract_unstemmed	Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. Consequently, the discovered trends will help the underlying social networks to further enhance their platforms for the betterment of the users as well as for their business growth.
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title_short	Online social network trend discovery using frequent subgraph mining
url	https://dx.doi.org/10.1007/s13278-020-00682-3
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up_date	2024-07-03T17:11:57.415Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR040624145</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220111185737.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13278-020-00682-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR040624145</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)SPRs13278-020-00682-3-e</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13278-020-00682-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rehman, Saif Ur</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Online social network trend discovery using frequent subgraph mining</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Graph mining has become a well-established discipline within the domain of data mining. It has received much interest over the last decade as advances in computer hardware have provided the processing power to enable large-scale graph data mining to be conducted. Frequent subgraph mining (FSM) plays a very significant role in graph mining, attracting a great deal of attention in different domains, such as Bioinformatics, web data mining and social networks. Online social networks (SNs) play an important role in today’s Internet. These social networks contain huge amounts of data and present a challenging problem. FSM has been used in SNs to identify the frequent pattern trends existing in the network. A frequent pattern trend is defined as a sequence of time-stamped occurrences (support) value for specific frequent pattern that exist in the data. For example, most active researchers, most visited web pages or users’ navigation patterns over the web are few to mention. In the past few years, social network trend mining has been an active area of research. Many graph mining algorithms have been proposed, but a very limited effort exists for capturing an important dimension of SNs, which is trends discovery. Therefore, this paper introduces a novel FSM approach, called A-RAFF (ARAnked Frequent pattern-growth Framework), to discovering and comparing the frequent pattern trends exist in the social network data. Furthermore, the social network frequent pattern trend analysis has been evaluated using two standard social networks, Facebook-like network and the famous MSNBC news network datasets. 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