Uniform-scale assessment of role minimization in bipartite networks and its application to access control

Liuyi Wu, Lijun Dong, Yi Wang, Feng Zhang, Victor E. Lee, Xiaojun Kang and Qingzhong Liang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 507, issue C, 381-397

Abstract: As an important kind of complex network models, bipartite network is widely used in many applications such as access control. The process of finding a set of structural communities in a bipartite network is called role mining, which has been extensively used to automatically generate roles for structural communities. Role minimization, aiming to get the fewest roles to reduce the administrative complexity of the access control, is the simplest and most popular form of role mining. Current assessment metrics of role minimization results are based on the absolute values, and cannot measure or compare the results from different algorithms and different datasets on a uniform scale. To address this problem, this study proposes a framework of mark-based evaluation for role minimization (MERM) to provide a normalized measure for different role minimization results on a uniform scale. According to MERM, for each dataset, three quantitative reference marks are established to mirror the level of role minimization results. The marks are entirely determined by the inherent features of a dataset but have nothing to do with any role mining algorithm. Based on the marks, a normalized score vector is constructed to estimate the performance of results, independent of the size of any dataset. MERM can assess varieties of role minimization results on a uniform scale. The experimental results show the effectiveness of MERM: the different results generated from nine algorithms on six datasets can be measured on a uniform scale.

Keywords: Complex network; Bipartite network; Access control; Role mining; Role minimization (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S037843711830596X
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:507:y:2018:i:c:p:381-397

DOI: 10.1016/j.physa.2018.05.052

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 ().