Predicting link directions using local directed path
Xiaojie Wang,
Xue Zhang,
Chengli Zhao,
Zheng Xie,
Shengjun Zhang and
Dongyun Yi
Physica A: Statistical Mechanics and its Applications, 2015, vol. 419, issue C, 260-267
Abstract:
Link prediction in directed network is attracting growing interest among many network scientists. Compared with predicting the existence of a link, determining its direction is more complicated. In this paper, we propose an efficient solution named Local Directed Path to predict link direction. By adding an extra ground node to the network, we solve the information loss problem in sparse network, which makes our method effective and robust. As a quasi-local method, our method can deal with large-scale networks in a reasonable time. Empirical analysis on real networks shows that our method can correctly predict link directions, which outperforms some local and global methods.
Keywords: Link prediction; Directed network; Local directed path (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437114008450
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:419:y:2015:i:c:p:260-267
DOI: 10.1016/j.physa.2014.10.007
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 ().