Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014 |
| Untertitel | 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 17-24 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781479944958 |
| Publikationsstatus | Veröffentlicht - 13 Jan. 2014 |
| Extern publiziert | Ja |
| Veranstaltung | 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014 - Orlando, USA / Vereinigte Staaten Dauer: 9 Dez. 2014 → 12 Dez. 2014 |
Publikationsreihe
| Name | IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings |
|---|
Abstract
The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done simply by doing further generation iterations or undo them. In our paper we present four methods to merge two graphs based on the Barabási-Albert-model, and five strategies to reverse them. First we compared these algorithms by edge preservation, which describes the ratio of the inner structure kept after altering. To check if hubs in the initial graphs are hubs in the resulting graphs as well, we used the node-degree rank correlation. Finally we tested how well the node-degree distribution follows the power-law function from the Barabási-Albert-model.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
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IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. S. 17-24 7009499 (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - On merging and dividing of Barabási-Albert-graphs
AU - Held, Pascal
AU - Dockhorn, Alexander
AU - Kruse, Rudolf
N1 - Publisher Copyright: © 2014 IEEE.
PY - 2014/1/13
Y1 - 2014/1/13
N2 - The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done simply by doing further generation iterations or undo them. In our paper we present four methods to merge two graphs based on the Barabási-Albert-model, and five strategies to reverse them. First we compared these algorithms by edge preservation, which describes the ratio of the inner structure kept after altering. To check if hubs in the initial graphs are hubs in the resulting graphs as well, we used the node-degree rank correlation. Finally we tested how well the node-degree distribution follows the power-law function from the Barabási-Albert-model.
AB - The Barabási-Albert-model is commonly used to generate scale-free graphs, like social networks. To generate dynamics in these networks, methods for altering such graphs are needed. Growing and shrinking is done simply by doing further generation iterations or undo them. In our paper we present four methods to merge two graphs based on the Barabási-Albert-model, and five strategies to reverse them. First we compared these algorithms by edge preservation, which describes the ratio of the inner structure kept after altering. To check if hubs in the initial graphs are hubs in the resulting graphs as well, we used the node-degree rank correlation. Finally we tested how well the node-degree distribution follows the power-law function from the Barabási-Albert-model.
UR - http://www.scopus.com/inward/record.url?scp=84946691455&partnerID=8YFLogxK
U2 - 10.1109/EALS.2014.7009499
DO - 10.1109/EALS.2014.7009499
M3 - Conference contribution
AN - SCOPUS:84946691455
T3 - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014: 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, Proceedings
SP - 17
EP - 24
BT - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - EALS 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Symposium on Evolving and Autonomous Learning Systems, EALS 2014
Y2 - 9 December 2014 through 12 December 2014
ER -