Information Technology Reference
In-Depth Information
10. Isella, L., Stehle, J., Barrat, A., Cattuto, C., Pinton, J.-F., Van den Broeck,
W.: What's in a crowd? Analysis of face-to-face behavioral networks. J. Theor.
Biol. 271, 166-180 (2010), doi:10.1016/j.jtbi.2010.11.033
11. Carley, K.: Destabilizing Terrorist Networks. In: Proceedings of the 8th Interna-
tional Command and Control Research and Technology Symposium. Conference
held at the National Defense War College, Washington DC. Evidence Based Re-
search, Track 3. Electronic Publication (2003)
12. Read, J.M., Eames, K.T.D., Edmunds, J.W.: Dynamic social networks and the im-
plications for the spread of infectious disease. Journal of the Royal Society Interface
the Royal Society 5(26), 1001-1007 (2008)
13. Skyrms, B., Pemantle, R.: A Dynamic Model of Social Network Formation. In:
Proceedings of the National Academy of Sciences (2004)
14. Kossinets, G., Watts, D.J.: Empirical Analysis of an Evolving Social Network.
Science 311(5757), 88-90 (2006), doi:10.1126/science.1116869
15. Tizghadam, A.: On Congestion Control in Mission Critical Networks. In: Proceed-
ings of the Second IEEE International Workshop on MissionCritical Networking
(2008)
16. Tantipathananandh, C., Berger-Wolf, T.Y., Kempe, D.: Knowledge Discovery and
Data Mining. In: A Framework for Community Identification in Dynamic Social
Networks, pp. 717-726 (2007), doi:10.1145/1281192.1281269
17. Barabási, A.-L., Albert, R.: Emergence of Scaling in Random Networks. Sci-
ence 286(5439), 509-512 (1999), doi:10.1126/science.286.5439.509
18. Erdós, P., Rényi, A.: On random graphs, I. Publicationes Mathematicae (Debre-
cen) 6, 290-297 (1959)
19. Lee, S., Rocha, L.E.C., Liljeros, F., Holme, P.: Exploiting temporal network struc-
tures of human interaction to effectively immunize populations. Quantitative Biol-
ogy - Populations and Evolution (November 2010)
20. The Kansas Event Data System: Gulf data set,
http://web.ku.edu/~keds/data.dir/gulf.html (accesssed February 25, 2013)
21. Pajek datasets: KEDS - The Kansas Event Data System,
http://vlado.fmf.uni-lj.si/pub/networks/data/KEDS/keds.htm
(accesssed February 25, 2013)
22. Rocha, L.E.C., Liljeros, F., Holme, P.: Information dynamics shape the sexual
networks of internet-mediated prostitution. Proc. Natl. Acad. Sci. 107, 5706-5711
(2010)
Appendix: Daily/Monthly Gulf Dataset Comparison
The following charts demonstrate the evolution of the properties for both the
dynamic cumulative and snapshot networks of the Gulf dataset. The left column
contains the results obtained from the daily granuality, while the right column
contains the same property for the monthly version. The properties of the cu-
mulative network does not show notable differences. On the other hand, the
snapshot network properties do show considerable differences. Since the snap-
shot network of the monthly dataset is about
30 times larger, this is not
surprising. This difference results in a higher overall rating for the properties,
and larger connected components.
Search WWH ::




Custom Search