Paper Info
Title
Competition for popularity in bipartite networks.
Abstract
We present a dynamical model for rewiring and attachment in bipartite networks. Edges are placed between nodes that belong to catalogs that can either be fixed in size or growing in size. The model is motivated by an empirical study of data from the video rental service Netflix, which invites its users to give ratings to the videos available in its catalog. We find that the distribution of the number of ratings given by users and that of the number of ratings received by videos both follow a power law with an exponential cutoff. We also examine the activity patterns of Netflix users and find bursts of intense video-rating activity followed by long periods of inactivity. We derive ordinary differential equations to model the acquisition of edges by the nodes over time and obtain the corresponding time-dependent degree distributions. We then compare our results with the Netflix data and find good agreement. We conclude with a discussion of how catalog models can be used to study systems in which agents are forced to choose, rate, or prioritize their interactions from a large set of options. (C) 2010 American Institute of Physics. [doi:10.1063/1.3475411]
Year
DOI
Venue
2009
10.1063/1.3475411
CHAOS
Keywords
Field
DocType
rate equations,bipartite networks,bursts,catalog networks,human dynamics,empirical study,ordinary differential equation,partial differential equation,power law,complex network,degree distribution,rate equation,social organization
Exponential function,Ordinary differential equation,Control theory,Popularity,Bipartite graph,Cutoff,Theoretical computer science,Complex network,Artificial intelligence,Power law,Mathematics,Empirical research
Journal
Volume
Issue
ISSN
20
4
1054-1500
Citations 
PageRank 
References 
5
0.49
10
Authors
3
Name
Order
Citations
PageRank
Mariano Beguerisse Díaz150.49
Mason A. Porter2536.75
Jukka-pekka Onnela347536.55