Tracing temporal communities and event prediction in dynamic social networks

Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over tim...
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Autor*in:	Khafaei, Taleb [verfasserIn] Tavakoli Taraghi, Alireza Hosseinzadeh, Mehdi Rezaee, Ali

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Event prediction Community feature Dynamic social networks Community tracing Temporal community

Anmerkung:	© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Übergeordnetes Werk:	Enthalten in: Social network analysis and mining - Wien : Springer, 2011, 9(2019), 1 vom: 03. Okt.
Übergeordnetes Werk:	volume:9 ; year:2019 ; number:1 ; day:03 ; month:10

Links:	Volltext

DOI / URN:	10.1007/s13278-019-0604-8

Katalog-ID:	SPR031187048

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520			\|a Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy.
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allfields	10.1007/s13278-019-0604-8 doi (DE-627)SPR031187048 (SPR)s13278-019-0604-8-e DE-627 ger DE-627 rakwb eng Khafaei, Taleb verfasserin aut Tracing temporal communities and event prediction in dynamic social networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2019 Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213 Tavakoli Taraghi, Alireza aut Hosseinzadeh, Mehdi aut Rezaee, Ali aut Enthalten in Social network analysis and mining Wien : Springer, 2011 9(2019), 1 vom: 03. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:9 year:2019 number:1 day:03 month:10 https://dx.doi.org/10.1007/s13278-019-0604-8 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 9 2019 1 03 10
spelling	10.1007/s13278-019-0604-8 doi (DE-627)SPR031187048 (SPR)s13278-019-0604-8-e DE-627 ger DE-627 rakwb eng Khafaei, Taleb verfasserin aut Tracing temporal communities and event prediction in dynamic social networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2019 Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213 Tavakoli Taraghi, Alireza aut Hosseinzadeh, Mehdi aut Rezaee, Ali aut Enthalten in Social network analysis and mining Wien : Springer, 2011 9(2019), 1 vom: 03. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:9 year:2019 number:1 day:03 month:10 https://dx.doi.org/10.1007/s13278-019-0604-8 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 9 2019 1 03 10
allfields_unstemmed	10.1007/s13278-019-0604-8 doi (DE-627)SPR031187048 (SPR)s13278-019-0604-8-e DE-627 ger DE-627 rakwb eng Khafaei, Taleb verfasserin aut Tracing temporal communities and event prediction in dynamic social networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2019 Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213 Tavakoli Taraghi, Alireza aut Hosseinzadeh, Mehdi aut Rezaee, Ali aut Enthalten in Social network analysis and mining Wien : Springer, 2011 9(2019), 1 vom: 03. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:9 year:2019 number:1 day:03 month:10 https://dx.doi.org/10.1007/s13278-019-0604-8 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 9 2019 1 03 10
allfieldsGer	10.1007/s13278-019-0604-8 doi (DE-627)SPR031187048 (SPR)s13278-019-0604-8-e DE-627 ger DE-627 rakwb eng Khafaei, Taleb verfasserin aut Tracing temporal communities and event prediction in dynamic social networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2019 Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213 Tavakoli Taraghi, Alireza aut Hosseinzadeh, Mehdi aut Rezaee, Ali aut Enthalten in Social network analysis and mining Wien : Springer, 2011 9(2019), 1 vom: 03. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:9 year:2019 number:1 day:03 month:10 https://dx.doi.org/10.1007/s13278-019-0604-8 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 9 2019 1 03 10
allfieldsSound	10.1007/s13278-019-0604-8 doi (DE-627)SPR031187048 (SPR)s13278-019-0604-8-e DE-627 ger DE-627 rakwb eng Khafaei, Taleb verfasserin aut Tracing temporal communities and event prediction in dynamic social networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Austria, part of Springer Nature 2019 Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213 Tavakoli Taraghi, Alireza aut Hosseinzadeh, Mehdi aut Rezaee, Ali aut Enthalten in Social network analysis and mining Wien : Springer, 2011 9(2019), 1 vom: 03. Okt. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:9 year:2019 number:1 day:03 month:10 https://dx.doi.org/10.1007/s13278-019-0604-8 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 9 2019 1 03 10
language	English
source	Enthalten in Social network analysis and mining 9(2019), 1 vom: 03. Okt. volume:9 year:2019 number:1 day:03 month:10
sourceStr	Enthalten in Social network analysis and mining 9(2019), 1 vom: 03. Okt. volume:9 year:2019 number:1 day:03 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Event prediction Community feature Dynamic social networks Community tracing Temporal community
isfreeaccess_bool	false
container_title	Social network analysis and mining
authorswithroles_txt_mv	Khafaei, Taleb @@aut@@ Tavakoli Taraghi, Alireza @@aut@@ Hosseinzadeh, Mehdi @@aut@@ Rezaee, Ali @@aut@@
publishDateDaySort_date	2019-10-03T00:00:00Z
hierarchy_top_id	647305739
id	SPR031187048
language_de	englisch
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author	Khafaei, Taleb
spellingShingle	Khafaei, Taleb misc Event prediction misc Community feature misc Dynamic social networks misc Community tracing misc Temporal community Tracing temporal communities and event prediction in dynamic social networks
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topic_title	Tracing temporal communities and event prediction in dynamic social networks Event prediction (dpeaa)DE-He213 Community feature (dpeaa)DE-He213 Dynamic social networks (dpeaa)DE-He213 Community tracing (dpeaa)DE-He213 Temporal community (dpeaa)DE-He213
topic	misc Event prediction misc Community feature misc Dynamic social networks misc Community tracing misc Temporal community
topic_unstemmed	misc Event prediction misc Community feature misc Dynamic social networks misc Community tracing misc Temporal community
topic_browse	misc Event prediction misc Community feature misc Dynamic social networks misc Community tracing misc Temporal community
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title	Tracing temporal communities and event prediction in dynamic social networks
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title_full	Tracing temporal communities and event prediction in dynamic social networks
author_sort	Khafaei, Taleb
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author_browse	Khafaei, Taleb Tavakoli Taraghi, Alireza Hosseinzadeh, Mehdi Rezaee, Ali
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doi_str_mv	10.1007/s13278-019-0604-8
title_sort	tracing temporal communities and event prediction in dynamic social networks
title_auth	Tracing temporal communities and event prediction in dynamic social networks
abstract	Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. © Springer-Verlag GmbH Austria, part of Springer Nature 2019
abstractGer	Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. © Springer-Verlag GmbH Austria, part of Springer Nature 2019
abstract_unstemmed	Abstract Online social networks (OSNs) represent the communication structure among users in the network. This communication structure is separated into segments of highly communicative density in comparison with the rest of the network as communities. The structure of a community may change over time due to changes in the relationships between its members or connection with other communities. These changes are known as community events. However, in this paper, we present the event predicting in dynamic social network (EPDSN) method to trace temporal communities. For this purpose, we propose a new definition of events for OSNs and identify types of events in compliance with the reality of OSNs. Features and events of the community during the previous snapshot are used as the input of the classifier into the learning model. We train the EPDSN method based on real-world Facebook, Wikipedia, and an OSN at the University of California datasets. This method uses the non-overlapping snapshots to keep the reality of the event prediction for the next snapshot and deploys snapshots with the same length to preserve its generality. The experimental results confirm that the prediction of the events for a community in dynamic OSNs can be achieved with high accuracy. © Springer-Verlag GmbH Austria, part of Springer Nature 2019
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title_short	Tracing temporal communities and event prediction in dynamic social networks
url	https://dx.doi.org/10.1007/s13278-019-0604-8
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author2	Tavakoli Taraghi, Alireza Hosseinzadeh, Mehdi Rezaee, Ali
author2Str	Tavakoli Taraghi, Alireza Hosseinzadeh, Mehdi Rezaee, Ali
ppnlink	647305739
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doi_str	10.1007/s13278-019-0604-8
up_date	2024-07-03T22:29:44.822Z
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