DANCer: dynamic attributed networks with community structure generation
Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one communit...
Ausführliche Beschreibung
Autor*in: |
Largeron, C. [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2017 |
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Schlagwörter: |
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Anmerkung: |
© Springer-Verlag London 2017 |
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Übergeordnetes Werk: |
Enthalten in: Knowledge and information systems - Springer London, 2000, 53(2017), 1 vom: 02. März, Seite 109-151 |
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Übergeordnetes Werk: |
volume:53 ; year:2017 ; number:1 ; day:02 ; month:03 ; pages:109-151 |
Links: |
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DOI / URN: |
10.1007/s10115-017-1028-2 |
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Katalog-ID: |
OLC2063382018 |
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520 | |a Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. | ||
650 | 4 | |a Social network | |
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10.1007/s10115-017-1028-2 doi (DE-627)OLC2063382018 (DE-He213)s10115-017-1028-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Largeron, C. verfasserin aut DANCer: dynamic attributed networks with community structure generation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2017 Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. Social network Graph generator Community structure Mougel, P. N. aut Benyahia, O. aut Zaïane, O. R. (orcid)0000-0002-0060-5988 aut Enthalten in Knowledge and information systems Springer London, 2000 53(2017), 1 vom: 02. März, Seite 109-151 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:53 year:2017 number:1 day:02 month:03 pages:109-151 https://doi.org/10.1007/s10115-017-1028-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 53 2017 1 02 03 109-151 |
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10.1007/s10115-017-1028-2 doi (DE-627)OLC2063382018 (DE-He213)s10115-017-1028-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Largeron, C. verfasserin aut DANCer: dynamic attributed networks with community structure generation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2017 Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. Social network Graph generator Community structure Mougel, P. N. aut Benyahia, O. aut Zaïane, O. R. (orcid)0000-0002-0060-5988 aut Enthalten in Knowledge and information systems Springer London, 2000 53(2017), 1 vom: 02. März, Seite 109-151 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:53 year:2017 number:1 day:02 month:03 pages:109-151 https://doi.org/10.1007/s10115-017-1028-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 53 2017 1 02 03 109-151 |
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10.1007/s10115-017-1028-2 doi (DE-627)OLC2063382018 (DE-He213)s10115-017-1028-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Largeron, C. verfasserin aut DANCer: dynamic attributed networks with community structure generation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2017 Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. Social network Graph generator Community structure Mougel, P. N. aut Benyahia, O. aut Zaïane, O. R. (orcid)0000-0002-0060-5988 aut Enthalten in Knowledge and information systems Springer London, 2000 53(2017), 1 vom: 02. März, Seite 109-151 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:53 year:2017 number:1 day:02 month:03 pages:109-151 https://doi.org/10.1007/s10115-017-1028-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 53 2017 1 02 03 109-151 |
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10.1007/s10115-017-1028-2 doi (DE-627)OLC2063382018 (DE-He213)s10115-017-1028-2-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 06.74$jInformationssysteme bkl 54.64$jDatenbanken bkl Largeron, C. verfasserin aut DANCer: dynamic attributed networks with community structure generation 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2017 Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. Social network Graph generator Community structure Mougel, P. N. aut Benyahia, O. aut Zaïane, O. R. (orcid)0000-0002-0060-5988 aut Enthalten in Knowledge and information systems Springer London, 2000 53(2017), 1 vom: 02. März, Seite 109-151 (DE-627)323971725 (DE-600)2036569-X (DE-576)9323971723 0219-1377 nnns volume:53 year:2017 number:1 day:02 month:03 pages:109-151 https://doi.org/10.1007/s10115-017-1028-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB GBV_ILN_70 06.74$jInformationssysteme VZ 106415212 (DE-625)106415212 54.64$jDatenbanken VZ 106410865 (DE-625)106410865 AR 53 2017 1 02 03 109-151 |
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dancer: dynamic attributed networks with community structure generation |
title_auth |
DANCer: dynamic attributed networks with community structure generation |
abstract |
Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. © Springer-Verlag London 2017 |
abstractGer |
Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. © Springer-Verlag London 2017 |
abstract_unstemmed |
Abstract Most networks, such as those generated from social media, tend to evolve gradually with frequent changes in the activity and the interactions of their participants. Furthermore, the communities inside the network can grow, shrink, merge, or split, and the entities can move from one community to another. The aim of community detection methods is precisely to detect the evolution of these communities. However, evaluating these algorithms requires tests on real or artificial networks with verifiable ground truth. Dynamic networks generators have been recently proposed for this task, but most of them consider only the structure of the network, disregarding the characteristics of the nodes. In this paper, we propose a new generator for dynamic attributed networks with community structure that follow the properties of real-world networks. The evolution of the network is performed using two kinds of operations: Micro-operations are applied on the edges and vertices, while macro-operations on the communities. Moreover, the properties of real-world networks such as preferential attachment or homophily are preserved during the evolution of the network, as confirmed by our experiments. © Springer-Verlag London 2017 |
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title_short |
DANCer: dynamic attributed networks with community structure generation |
url |
https://doi.org/10.1007/s10115-017-1028-2 |
remote_bool |
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author2 |
Mougel, P. N. Benyahia, O. Zaïane, O. R. |
author2Str |
Mougel, P. N. Benyahia, O. Zaïane, O. R. |
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doi_str |
10.1007/s10115-017-1028-2 |
up_date |
2024-07-03T18:50:00.144Z |
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