Link prediction in a user–object network based on time-weighted resource allocation

Ji Liu and Guishi Deng

Physica A: Statistical Mechanics and its Applications, 2009, vol. 388, issue 17, 3643-3650

Abstract: Human dynamics has attracted much attention in recent years. Quantitative understanding of the statistical mechanics of human behavior in an online network is a new challenge for researchers. In an online network, users’ behaviors can be abstracted and projected into a user–object network. Many complex problems concerning resource diffusion, such as recommendation system, network flow and social network behavior, can be solved partially by this user–object network. Although some researchers have given some statistical description of the network recently, little work has been done on link prediction in a user–object network. The objective of this paper is to predict new links based on historical ones in a user–object network. When link weight is taken into consideration, we find that both time attenuation and diversion delay play key roles in link prediction in an user–object network. We then combine these two time effect factors of link weight with users’ lifespans and construct the time-weighted network (TWN) model on the basis of resource allocation. Experimental results show the TWN model can greatly enhance the link prediction accuracy.

Keywords: Human dynamics; Bipartite network; Resource allocation; Time attenuation; Diversion delay (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437109004099
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:388:y:2009:i:17:p:3643-3650

DOI: 10.1016/j.physa.2009.05.021

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().