Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 2nd European Network Intelligence Conference, ENIC 2015 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 67-74 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781467375924 |
| Publikationsstatus | Veröffentlicht - 5 Nov. 2015 |
| Extern publiziert | Ja |
| Veranstaltung | 2nd European Network Intelligence Conference, ENIC 2015 - Karlskrona, Schweiz Dauer: 21 Sept. 2015 → 22 Sept. 2015 |
Publikationsreihe
| Name | Proceedings - 2nd European Network Intelligence Conference, ENIC 2015 |
|---|
Abstract
Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple ant colonies are competing for the available nodes. Each hive creates ants, which will explore nearby graph structures and drop hive-specific pheromones on visited nodes. Over time, hives will collect nodes and will be relocated to the center of all collected nodes. In case of dynamic graph clustering, pheromone values can be reused in consecutive iterations. Our evaluation revealed that the proposed algorithm can lead to results on a par with the k-median algorithm and performs worse than Louvain clustering. However competing ant hives have the advantage of implicit noise detection, which comes at the cost of longer computation times. This can make it a suitable choice for certain clustering tasks.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Information systems
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Proceedings - 2nd European Network Intelligence Conference, ENIC 2015. Institute of Electrical and Electronics Engineers Inc., 2015. S. 67-74 7321238 (Proceedings - 2nd European Network Intelligence Conference, ENIC 2015).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Clustering Social Networks Using Competing Ant Hives
AU - Held, Pascal
AU - Dockhorn, Alexander
AU - Krause, Benjamin
AU - Kruse, Rudolf
N1 - Publisher Copyright: © 2015 IEEE.
PY - 2015/11/5
Y1 - 2015/11/5
N2 - Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple ant colonies are competing for the available nodes. Each hive creates ants, which will explore nearby graph structures and drop hive-specific pheromones on visited nodes. Over time, hives will collect nodes and will be relocated to the center of all collected nodes. In case of dynamic graph clustering, pheromone values can be reused in consecutive iterations. Our evaluation revealed that the proposed algorithm can lead to results on a par with the k-median algorithm and performs worse than Louvain clustering. However competing ant hives have the advantage of implicit noise detection, which comes at the cost of longer computation times. This can make it a suitable choice for certain clustering tasks.
AB - Methods for clustering static graphs cannot always be transferred straight forward to dynamic scenarios. A typical approach is to reduce the number of updates by reusing results of previous iterations. But are there natural ways to implement dynamic graph clustering? This paper proposes a method, which was derived by graph based ant colony algorithms. Similar to other clustering algorithms, multiple ant colonies are competing for the available nodes. Each hive creates ants, which will explore nearby graph structures and drop hive-specific pheromones on visited nodes. Over time, hives will collect nodes and will be relocated to the center of all collected nodes. In case of dynamic graph clustering, pheromone values can be reused in consecutive iterations. Our evaluation revealed that the proposed algorithm can lead to results on a par with the k-median algorithm and performs worse than Louvain clustering. However competing ant hives have the advantage of implicit noise detection, which comes at the cost of longer computation times. This can make it a suitable choice for certain clustering tasks.
KW - Ant Hives
KW - Clustering
KW - Community Detection
KW - Social Network Analysis
UR - http://www.scopus.com/inward/record.url?scp=84964612377&partnerID=8YFLogxK
U2 - 10.1109/ENIC.2015.18
DO - 10.1109/ENIC.2015.18
M3 - Conference contribution
AN - SCOPUS:84964612377
T3 - Proceedings - 2nd European Network Intelligence Conference, ENIC 2015
SP - 67
EP - 74
BT - Proceedings - 2nd European Network Intelligence Conference, ENIC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd European Network Intelligence Conference, ENIC 2015
Y2 - 21 September 2015 through 22 September 2015
ER -