2022
DOI: 10.1109/tcyb.2021.3051606
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Vicinal Vertex Allocation for Matrix Factorization in Networks

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Cited by 11 publications
(6 citation statements)
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References 42 publications
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“…for j in LC do (7) if OS ij > OS then (8) OC.append(i ∪ j) (9) end if (10) end for (11) end for (12) ♯ Isolated nodes adjustment (13) for i in OC do (14) if len(i) � � 0 and otherSide(i[0]) then (15) v…”
Section: Community Detection Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…for j in LC do (7) if OS ij > OS then (8) OC.append(i ∪ j) (9) end if (10) end for (11) end for (12) ♯ Isolated nodes adjustment (13) for i in OC do (14) if len(i) � � 0 and otherSide(i[0]) then (15) v…”
Section: Community Detection Resultsmentioning
confidence: 99%
“…Overlapping community detection in complex networks has attracted the attention of many scholars and achieved many results [7], such as LFM [8], COPRA [9], and LINK [10]. However, most overlapping community algorithms lack effective seed selection and community optimization methods, and these algorithms often get community results with low accuracy [11]. To solve these problems, this paper proposes an overlapping community detection algorithm based on information fusion.…”
Section: Introductionmentioning
confidence: 99%
“…Rotation forest (ROF) is a popular ensemble classifier firstly proposed by Rodriguez et al (2006) . Compared with other classifiers, the ROF model is successfully used in dealing with many computational biology problems ( He et al, 2021b ). The basic idea of the rotation forest model is to simultaneously improve both individual accuracy and member diversity within an ensemble classifier.…”
Section: Materials and Methodologymentioning
confidence: 99%
“…Label propagation-based algorithms use label information for user segmentation; however, these algorithms may produce community labels that do not match the real node attributes [40]. Compared with previous types of user segmentation algorithms, user node embedding-based algorithms can better preserve the complex information contained in the network and provide better accuracy of user segmentation results [18,41].…”
Section: User Segmentation In Oicsmentioning
confidence: 99%