HM-EIICT: Fairness-aware link prediction in complex networks using community information
Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this wo...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Saxena, Akrati [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of combinatorial optimization - Springer US, 1997, 44(2021), 4 vom: 27. Aug., Seite 2853-2870 |
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| Übergeordnetes Werk: |
volume:44 ; year:2021 ; number:4 ; day:27 ; month:08 ; pages:2853-2870 |
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| DOI / URN: |
10.1007/s10878-021-00788-0 |
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OLC2079734199 |
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| 520 | |a Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. | ||
| 650 | 4 | |a Link prediction | |
| 650 | 4 | |a Link analysis | |
| 650 | 4 | |a Similarity-based indices | |
| 650 | 4 | |a Social networks | |
| 700 | 1 | |a Fletcher, George |4 aut | |
| 700 | 1 | |a Pechenizkiy, Mykola |4 aut | |
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10.1007/s10878-021-00788-0 doi (DE-627)OLC2079734199 (DE-He213)s10878-021-00788-0-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Saxena, Akrati verfasserin aut HM-EIICT: Fairness-aware link prediction in complex networks using community information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. Link prediction Link analysis Similarity-based indices Social networks Fletcher, George aut Pechenizkiy, Mykola aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2021), 4 vom: 27. Aug., Seite 2853-2870 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2021 number:4 day:27 month:08 pages:2853-2870 https://doi.org/10.1007/s10878-021-00788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2021 4 27 08 2853-2870 |
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10.1007/s10878-021-00788-0 doi (DE-627)OLC2079734199 (DE-He213)s10878-021-00788-0-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Saxena, Akrati verfasserin aut HM-EIICT: Fairness-aware link prediction in complex networks using community information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. Link prediction Link analysis Similarity-based indices Social networks Fletcher, George aut Pechenizkiy, Mykola aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2021), 4 vom: 27. Aug., Seite 2853-2870 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2021 number:4 day:27 month:08 pages:2853-2870 https://doi.org/10.1007/s10878-021-00788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2021 4 27 08 2853-2870 |
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10.1007/s10878-021-00788-0 doi (DE-627)OLC2079734199 (DE-He213)s10878-021-00788-0-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Saxena, Akrati verfasserin aut HM-EIICT: Fairness-aware link prediction in complex networks using community information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. Link prediction Link analysis Similarity-based indices Social networks Fletcher, George aut Pechenizkiy, Mykola aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2021), 4 vom: 27. Aug., Seite 2853-2870 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2021 number:4 day:27 month:08 pages:2853-2870 https://doi.org/10.1007/s10878-021-00788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2021 4 27 08 2853-2870 |
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10.1007/s10878-021-00788-0 doi (DE-627)OLC2079734199 (DE-He213)s10878-021-00788-0-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Saxena, Akrati verfasserin aut HM-EIICT: Fairness-aware link prediction in complex networks using community information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. Link prediction Link analysis Similarity-based indices Social networks Fletcher, George aut Pechenizkiy, Mykola aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2021), 4 vom: 27. Aug., Seite 2853-2870 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2021 number:4 day:27 month:08 pages:2853-2870 https://doi.org/10.1007/s10878-021-00788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2021 4 27 08 2853-2870 |
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10.1007/s10878-021-00788-0 doi (DE-627)OLC2079734199 (DE-He213)s10878-021-00788-0-p DE-627 ger DE-627 rakwb eng 510 VZ 3,2 ssgn Saxena, Akrati verfasserin aut HM-EIICT: Fairness-aware link prediction in complex networks using community information 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. Link prediction Link analysis Similarity-based indices Social networks Fletcher, George aut Pechenizkiy, Mykola aut Enthalten in Journal of combinatorial optimization Springer US, 1997 44(2021), 4 vom: 27. Aug., Seite 2853-2870 (DE-627)216539323 (DE-600)1339574-9 (DE-576)094421935 1382-6905 nnns volume:44 year:2021 number:4 day:27 month:08 pages:2853-2870 https://doi.org/10.1007/s10878-021-00788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW SSG-OPC-MAT GBV_ILN_2108 AR 44 2021 4 27 08 2853-2870 |
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Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. © The Author(s) 2021 |
| abstractGer |
Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. © The Author(s) 2021 |
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Abstract The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions. © The Author(s) 2021 |
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HM-EIICT: Fairness-aware link prediction in complex networks using community information |
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https://doi.org/10.1007/s10878-021-00788-0 |
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Fletcher, George Pechenizkiy, Mykola |
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