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

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

doi:10.1016/j.physa.2018.05.052

Cited by 5 publications

(7 citation statements)

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“…For instance, from a bipartite network of scientists and papers, one can extract a network of scientists only, who are related by coauthorship. However, such a projection loses a lot of information and leads to an oversimplified and less useful representation [6,19,24]. erefore, we refine it in an alternative way below.…”

Section: An Easy Casementioning

confidence: 99%

“…According to the definition of a community [6,26,27], there are many edges within communities but few edges between communities. Modularity [17,28] is the most popular function to measure the division quality of a network.…”

Section: Relation Between Similarity Correlation and Community's Edgesmentioning

confidence: 99%

“…e authors of [15,16] maximized Barber's bipartite modularity for bipartite community detection. However, maximizing both modularities noted above proved to be a NP-hard problem [6,18]. e bipartite network communities generated in the previous studies are of mixed type, and so far, there is no exploration inferring to the numbers of pure-type communities in a bipartite network.…”

Section: Introductionmentioning

confidence: 99%

“…(b) QFC(ϕ) of the events filtering network. We get the minimum QFC(ϕ) � 1 when ϕ best � 0.464 at the red point 6. Scientific Programming matrix.…”

mentioning

confidence: 94%

“…Beginning from Newman's [5] study, community detection has attracted considerable attention from researchers [6], aiming to identify good ways to divide up a network into communities. A range of powerful and flexible methods for dividing a bipartite network into a specified number of communities have been proposed in recent years [4,7,8].…”

Section: Introductionmentioning

confidence: 99%

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New Community Estimation Method in Bipartite Networks Based on Quality of Filtering Coefficient

Wang

Liu

2019

Scientific Programming

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Community detection is an important task in network analysis, in which we aim to find a network partitioning that groups together vertices with similar community-level connectivity patterns. Bipartite networks are a common type of network in which there are two types of vertices, and only vertices of different types can be connected. While there are a range of powerful and flexible methods for dividing a bipartite network into a specified number of communities, it is an open question how to determine exactly how many communities one should use, and estimating the numbers of pure-type communities in a bipartite network has not been completed. In our paper, we propose a method named as “biCNEQ” (bipartite network communities number estimation based on quality of filtering coefficient), which ensures that communities are all pure type, for estimating the number of communities in a bipartite network. This paper makes the following contributions: (1) we show how a unipartite weighted network, which we call similarity network, can be projected from a bipartite network using a measure of correlation; (2) we reveal the relation between the similarity correlation and community’s edges in the vertices of a unipartite network; (3) we design a measure of the filtering quality named QFC (quality of filtering coefficient) to filter the similarity network and construct a binary network, which we call approximation network; and (4) the number of communities in each type of unipartite networks is estimated using Riolo’s method with the approximation network as input. Finally, the proposed biCNEQ is demonstrated by both synthetic bipartite networks and a real-world network, and the results show that it can determine the correct number of communities and perform better than two classical one-mode projection methods.

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Section: An Easy Casementioning

confidence: 99%