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
Gespeichert in:
| 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: |
|---|
| DOI / URN: |
10.1007/s10115-017-1028-2 |
|---|
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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. | ||
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Largeron, C. |
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dancer: dynamic attributed networks with community structure generation |
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DANCer: dynamic attributed networks with community structure generation |
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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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DANCer: dynamic attributed networks with community structure generation |
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