Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 23-49 |
| Seitenumfang | 27 |
| Fachzeitschrift | Journal of Artificial Intelligence and Soft Computing Research |
| Jahrgang | 5 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 1 Jan. 2015 |
| Extern publiziert | Ja |
Abstract
Modeling social interaction can be based on graphs. However most models lack the flexibility of including larger changes over time. The Barabási-Albert-model is a generative model which already offers mechanisms for adding nodes. We will extent this by presenting four methods for merging and five for dividing graphs based on the Barabási-Albert-model. Our algorithms were motivated by different real world scenarios and focus on preserving graph properties derived from these scenarios. With little alterations in the parameter estimation those algorithms can be used for other graph models as well. All algorithms were tested in multiple experiments using graphs based on the Barabási-Albert-model, an extended version of the Barabási-Albert-model by Holme and Kim, the Watts-Strogatz-model and the Erdos-Rényi-model. Furthermore we concluded that our algorithms are able to preserve different properties of graphs independently from the used model. To support the choice of algorithm, we created a guideline which highlights advantages and disadvantages of discussed methods and their possible use-cases.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Mathematik (insg.)
- Modellierung und Simulation
- Informatik (insg.)
- Hardware und Architektur
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Artificial intelligence
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Journal of Artificial Intelligence and Soft Computing Research, Jahrgang 5, Nr. 1, 01.01.2015, S. 23-49.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - On merging and dividing social graphs
AU - Held, Pascal
AU - Dockhorn, Alexander
AU - Kruse, Rudolf
N1 - Publisher Copyright: © 2015 Sciendo. All rights reserved.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - Modeling social interaction can be based on graphs. However most models lack the flexibility of including larger changes over time. The Barabási-Albert-model is a generative model which already offers mechanisms for adding nodes. We will extent this by presenting four methods for merging and five for dividing graphs based on the Barabási-Albert-model. Our algorithms were motivated by different real world scenarios and focus on preserving graph properties derived from these scenarios. With little alterations in the parameter estimation those algorithms can be used for other graph models as well. All algorithms were tested in multiple experiments using graphs based on the Barabási-Albert-model, an extended version of the Barabási-Albert-model by Holme and Kim, the Watts-Strogatz-model and the Erdos-Rényi-model. Furthermore we concluded that our algorithms are able to preserve different properties of graphs independently from the used model. To support the choice of algorithm, we created a guideline which highlights advantages and disadvantages of discussed methods and their possible use-cases.
AB - Modeling social interaction can be based on graphs. However most models lack the flexibility of including larger changes over time. The Barabási-Albert-model is a generative model which already offers mechanisms for adding nodes. We will extent this by presenting four methods for merging and five for dividing graphs based on the Barabási-Albert-model. Our algorithms were motivated by different real world scenarios and focus on preserving graph properties derived from these scenarios. With little alterations in the parameter estimation those algorithms can be used for other graph models as well. All algorithms were tested in multiple experiments using graphs based on the Barabási-Albert-model, an extended version of the Barabási-Albert-model by Holme and Kim, the Watts-Strogatz-model and the Erdos-Rényi-model. Furthermore we concluded that our algorithms are able to preserve different properties of graphs independently from the used model. To support the choice of algorithm, we created a guideline which highlights advantages and disadvantages of discussed methods and their possible use-cases.
UR - http://www.scopus.com/inward/record.url?scp=84977479966&partnerID=8YFLogxK
U2 - 10.1515/jaiscr-2015-0017
DO - 10.1515/jaiscr-2015-0017
M3 - Article
AN - SCOPUS:84977479966
VL - 5
SP - 23
EP - 49
JO - Journal of Artificial Intelligence and Soft Computing Research
JF - Journal of Artificial Intelligence and Soft Computing Research
IS - 1
ER -