Preferred Attachment in Affiliation Networks

Bloznelis M, Götze F (2014)
Journal of Statistical Physics 156(4): 800-821.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor/in
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Abstract / Bemerkung
Vertices of an affiliation network are linked to attributes and two vertices are declared adjacent whenever they share a common attribute. For example, two customers of an internet shop (or video-sharing website) are called adjacent if they have purchased (or downloaded) the same or similar items. Assuming that each newly arrived customer is linked preferentially to already popular items we obtain a preferred attachment affiliation network that evolves in time. We show that the fraction of customers having neighbours scales as for large . Here is the ratio between the two intensities: intensity of the flow of customers and that of the newly arriving items.
Stichworte
Power; Degree sequence; Affiliation network; Preferential attachment; Random intersection graph; law
Erscheinungsjahr
2014
Zeitschriftentitel
Journal of Statistical Physics
Band
156
Ausgabe
4
Seite(n)
800-821
ISSN
0022-4715
Page URI
https://pub.uni-bielefeld.de/record/2690746

Zitieren

Bloznelis M, Götze F. Preferred Attachment in Affiliation Networks. Journal of Statistical Physics. 2014;156(4):800-821.
Bloznelis, M., & Götze, F. (2014). Preferred Attachment in Affiliation Networks. Journal of Statistical Physics, 156(4), 800-821. doi:10.1007/s10955-014-1033-8
Bloznelis, M., and Götze, F. (2014). Preferred Attachment in Affiliation Networks. Journal of Statistical Physics 156, 800-821.
Bloznelis, M., & Götze, F., 2014. Preferred Attachment in Affiliation Networks. Journal of Statistical Physics, 156(4), p 800-821.
M. Bloznelis and F. Götze, “Preferred Attachment in Affiliation Networks”, Journal of Statistical Physics, vol. 156, 2014, pp. 800-821.
Bloznelis, M., Götze, F.: Preferred Attachment in Affiliation Networks. Journal of Statistical Physics. 156, 800-821 (2014).
Bloznelis, Mindaugas, and Götze, Friedrich. “Preferred Attachment in Affiliation Networks”. Journal of Statistical Physics 156.4 (2014): 800-821.