CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique
Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been prop...
Ausführliche Beschreibung
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| Autor*in: |
Saxena, Rahul [verfasserIn] Paira, Pranjal [verfasserIn] Jadeja, Mahipal [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Social network analysis and mining - Springer Vienna, 2011, 14(2024), 1 vom: 12. Apr. |
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| Übergeordnetes Werk: |
volume:14 ; year:2024 ; number:1 ; day:12 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s13278-024-01244-7 |
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SPR055504310 |
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| 520 | |a Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. | ||
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| 700 | 1 | |a Jadeja, Mahipal |e verfasserin |4 aut | |
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10.1007/s13278-024-01244-7 doi (DE-627)SPR055504310 (SPR)s13278-024-01244-7-e DE-627 ger DE-627 rakwb eng 004 VZ Saxena, Rahul verfasserin aut CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. Graph theory (dpeaa)DE-He213 Information diffusion (dpeaa)DE-He213 Betweenness centrality (dpeaa)DE-He213 Clustering coefficient (dpeaa)DE-He213 Graph convolutional networks (dpeaa)DE-He213 Paira, Pranjal verfasserin aut Jadeja, Mahipal verfasserin aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 12. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:12 month:04 https://dx.doi.org/10.1007/s13278-024-01244-7 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 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_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 14 2024 1 12 04 |
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10.1007/s13278-024-01244-7 doi (DE-627)SPR055504310 (SPR)s13278-024-01244-7-e DE-627 ger DE-627 rakwb eng 004 VZ Saxena, Rahul verfasserin aut CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. Graph theory (dpeaa)DE-He213 Information diffusion (dpeaa)DE-He213 Betweenness centrality (dpeaa)DE-He213 Clustering coefficient (dpeaa)DE-He213 Graph convolutional networks (dpeaa)DE-He213 Paira, Pranjal verfasserin aut Jadeja, Mahipal verfasserin aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 12. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:12 month:04 https://dx.doi.org/10.1007/s13278-024-01244-7 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 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_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 14 2024 1 12 04 |
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10.1007/s13278-024-01244-7 doi (DE-627)SPR055504310 (SPR)s13278-024-01244-7-e DE-627 ger DE-627 rakwb eng 004 VZ Saxena, Rahul verfasserin aut CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. Graph theory (dpeaa)DE-He213 Information diffusion (dpeaa)DE-He213 Betweenness centrality (dpeaa)DE-He213 Clustering coefficient (dpeaa)DE-He213 Graph convolutional networks (dpeaa)DE-He213 Paira, Pranjal verfasserin aut Jadeja, Mahipal verfasserin aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 12. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:12 month:04 https://dx.doi.org/10.1007/s13278-024-01244-7 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 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_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 14 2024 1 12 04 |
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10.1007/s13278-024-01244-7 doi (DE-627)SPR055504310 (SPR)s13278-024-01244-7-e DE-627 ger DE-627 rakwb eng 004 VZ Saxena, Rahul verfasserin aut CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. Graph theory (dpeaa)DE-He213 Information diffusion (dpeaa)DE-He213 Betweenness centrality (dpeaa)DE-He213 Clustering coefficient (dpeaa)DE-He213 Graph convolutional networks (dpeaa)DE-He213 Paira, Pranjal verfasserin aut Jadeja, Mahipal verfasserin aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 12. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:12 month:04 https://dx.doi.org/10.1007/s13278-024-01244-7 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 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_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 14 2024 1 12 04 |
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10.1007/s13278-024-01244-7 doi (DE-627)SPR055504310 (SPR)s13278-024-01244-7-e DE-627 ger DE-627 rakwb eng 004 VZ Saxena, Rahul verfasserin aut CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. Graph theory (dpeaa)DE-He213 Information diffusion (dpeaa)DE-He213 Betweenness centrality (dpeaa)DE-He213 Clustering coefficient (dpeaa)DE-He213 Graph convolutional networks (dpeaa)DE-He213 Paira, Pranjal verfasserin aut Jadeja, Mahipal verfasserin aut Enthalten in Social network analysis and mining Springer Vienna, 2011 14(2024), 1 vom: 12. Apr. (DE-627)647305739 (DE-600)2595306-0 1869-5469 nnns volume:14 year:2024 number:1 day:12 month:04 https://dx.doi.org/10.1007/s13278-024-01244-7 X:VERLAG 0 lizenzpflichtig Volltext SYSFLAG_0 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_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 14 2024 1 12 04 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. 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CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique |
| abstract |
Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Theories to maximise influence in social networks have been investigated vastly by researchers in recent times. Various strategies have been proposed to identify initial seed node set to diffuse the information in the network. In this manuscript, a seed node selection strategy has been proposed based on the clustering coefficient and betweenness centrality scores of the nodes in the graph (CLUS-BET). The influence spread (in the underlying network) is measured using Independent Cascade Model (ICM) by selecting top k seed nodes having high CLUS-BET score. The network coverage obtained are $$72.48\%$$, $$65.85\%$$, $$51.12\%$$ and $$53.15\%$$ for CORA, Citeseer, PubMed and Amazon Computers network respectively. The results obtained are better in comparison to any of the classical centrality algorithms’-based seed node selection. Also, the results are compared with a recent state of art method GRAIN based seed selection strategy. The manuscript further testifies that the quality of nodes selected based on CLUS-BET property improves the Graph Convolution Network (GCN) based node classification accuracy. CLUS-BET based GCN produces a node classification accuracy of $$90\%$$, $$77\%$$ and $$89.2\%$$ for CORA, Citeseer and PubMed networks respectively. The results surpass the classification accuracy of base GCN model and its variations in contemporary state of art. Also, the property helps in identifying the most prominent influential clusters or groups of the network responsible for spreading the information in the network to a vast majority. The analytical explanation suggests that these are the groups having most number of nodes with high CLUS-BET score. The proposed idea is scalable with the graphical network as it avoids Monte Carlo simulation rounds with Greedy method for information cascade to identify seed nodes. The metric calculation for CLUS-BET is simple, less time consuming and effective. © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
CLUS-BET: improving influence propagation and classification in networks using a novel seed selection technique |
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https://dx.doi.org/10.1007/s13278-024-01244-7 |
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Paira, Pranjal Jadeja, Mahipal |
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10.1007/s13278-024-01244-7 |
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| score |
7.401884 |