Details
| Original language | English |
|---|---|
| Title of host publication | Information Processing and Management of Uncertainty in Knowledge-Based Systems - 15th International Conference, IPMU 2014, Proceedings |
| Publisher | Springer International Publishing AG |
| Pages | 46-55 |
| Number of pages | 10 |
| Edition | PART 2 |
| ISBN (print) | 9783319088549 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2014 - Montpellier, France Duration: 15 Jul 2014 → 19 Jul 2014 |
Publication series
| Name | Communications in Computer and Information Science |
|---|---|
| Number | PART 2 |
| Volume | 443 CCIS |
| ISSN (Print) | 1865-0929 |
Abstract
Social Network Analysis in the last decade has gained remarkable attention. The current analysis focuses more and more on the dynamic behavior of them. The underlying structure from Social Networks, like facebook, or twitter, can change over time. Groups can be merged or single nodes can move from one group to another. But these phenomenas do not only occur in social networks but also in human brains. The research in neural spike trains also focuses on finding functional communities. These communities can change over time by switching the stimuli presented to the subject. In this paper we introduce a data generator to create such dynamic behavior, with effects in the interactions between nodes. We generate time stamps for events for one-to-one, one-to-many, and many-to-all relations. This data could be used to demonstrate the functionality of algorithms on such data, e.g. clustering or visualization algorithms. We demonstrated that the generated data fulfills common properties of social networks.
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Mathematics(all)
- General Mathematics
Cite this
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Information Processing and Management of Uncertainty in Knowledge-Based Systems - 15th International Conference, IPMU 2014, Proceedings. PART 2. ed. Springer International Publishing AG, 2014. p. 46-55 (Communications in Computer and Information Science; Vol. 443 CCIS, No. PART 2).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Generating Events for Dynamic Social Network Simulations
AU - Held, Pascal
AU - Dockhorn, Alexander
AU - Kruse, Rudolf
PY - 2014
Y1 - 2014
N2 - Social Network Analysis in the last decade has gained remarkable attention. The current analysis focuses more and more on the dynamic behavior of them. The underlying structure from Social Networks, like facebook, or twitter, can change over time. Groups can be merged or single nodes can move from one group to another. But these phenomenas do not only occur in social networks but also in human brains. The research in neural spike trains also focuses on finding functional communities. These communities can change over time by switching the stimuli presented to the subject. In this paper we introduce a data generator to create such dynamic behavior, with effects in the interactions between nodes. We generate time stamps for events for one-to-one, one-to-many, and many-to-all relations. This data could be used to demonstrate the functionality of algorithms on such data, e.g. clustering or visualization algorithms. We demonstrated that the generated data fulfills common properties of social networks.
AB - Social Network Analysis in the last decade has gained remarkable attention. The current analysis focuses more and more on the dynamic behavior of them. The underlying structure from Social Networks, like facebook, or twitter, can change over time. Groups can be merged or single nodes can move from one group to another. But these phenomenas do not only occur in social networks but also in human brains. The research in neural spike trains also focuses on finding functional communities. These communities can change over time by switching the stimuli presented to the subject. In this paper we introduce a data generator to create such dynamic behavior, with effects in the interactions between nodes. We generate time stamps for events for one-to-one, one-to-many, and many-to-all relations. This data could be used to demonstrate the functionality of algorithms on such data, e.g. clustering or visualization algorithms. We demonstrated that the generated data fulfills common properties of social networks.
UR - http://www.scopus.com/inward/record.url?scp=84905013829&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-08855-6_6
DO - 10.1007/978-3-319-08855-6_6
M3 - Conference contribution
AN - SCOPUS:84905013829
SN - 9783319088549
T3 - Communications in Computer and Information Science
SP - 46
EP - 55
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 15th International Conference, IPMU 2014, Proceedings
PB - Springer International Publishing AG
T2 - 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, IPMU 2014
Y2 - 15 July 2014 through 19 July 2014
ER -