Eur. Phys. J. B (2012) 85: 286
https://doi.org/10.1140/epjb/e2012-30095-1

Regular Article

Heat conduction information filtering via local information of bipartite networks

¹ Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P.R. China
² Department of Physics, University of Fribourg, 1700 Fribourg, Switzerland
³ CABDyN Complexity Center, Saïd Business School, University of Oxford, Park End Street, Oxford, OX1 1HP, UK

^a e-mail: jianguo.liu@sbs.ox.ac.uk

Received: 19 December 2011
Received in final form: 5 May 2012
Published online: 20 August 2012

Abstract

Information filtering based on structure properties of user-object bipartite networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate the framework of heat-conduction-based (HC) information filtering [Y.-C. Zhang et al., Phys. Rev. Lett. 99, 154301 (2007)] in terms of the local node similarity. We compare nine well-known local similarity measures on four real networks. The results indicate that the local-heat-conduction-based similarity has the best accuracy and diversity simultaneously. Embedding the object degree effect into the heat conduction process, we present a new user similarity measure. Experimental results on four real networks demonstrate that the improved similarity measure could generate remarkably higher diversity and novelty results than the state-of-the-art HC information filtering algorithms based on local information, and the accuracy is also increased greatly or approximately unchanged. Since the improved similarity index only need the local information of user-object bipartite networks, it is therefore a strong candidate for potential application in information filtering of large-scale bipartite networks.