Vicinal Vertex Allocation for Matrix Factorization in Networks

He, Tiantian; Bai, Liang; Ong, Yew-Soon

doi:10.1109/tcyb.2021.3051606

Cited by 10 publications

(6 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

See 1 more Smart Citation

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

View full text Add to dashboard Cite

Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

show abstract

Section: Community Detection Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

View full text Add to dashboard Cite

show abstract

“…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%

CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information

Zhang

Cheng

2022

Front. Genet.

View full text Add to dashboard Cite

Identification and characterization of plant protein–protein interactions (PPIs) are critical in elucidating the functions of proteins and molecular mechanisms in a plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually expensive and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining the position-specific scoring matrix (PSSM), local optimal-oriented pattern (LOOP), and ensemble rotation forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, the ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets: Arabidopsis thaliana, Zea mays, and Oryza sativa. The experimental results demonstrate that the CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that the CPIELA approach could become a useful tool for facilitating the identification of possible plant PPIs.

show abstract

“…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%

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

Daradkeh

2022

Journal of Open Innovation: Technology, Market, and Complexity

View full text Add to dashboard Cite

The heterogeneity and diversity of users and external knowledge resources is a hallmark of open innovation communities (OICs). Although user segmentation in heterogeneous OICs is a prominent and recurring issue, it has received limited attention in open innovation research and practice. Most existing user segmentation methods ignore the heterogeneity and embedded relationships that link users to communities through various items, resulting in limited accuracy of user segmentation. In this study, we propose a user segmentation method in heterogeneous OICs based on multilayer information fusion and attention mechanisms. Our method stratifies the OIC and creates user node embeddings based on different relationship types. Node embeddings from different layers are then merged to form a global representation of user fusion embeddings based on a semantic attention mechanism. The embedding learning of nodes is optimized using a multi-objective optimized node representation based on the Deep Graph Infomax (DGI) algorithm. Finally, the k-means algorithm is used to form clusters of users and partition them into distinct segments based on shared features. Experiments conducted on datasets collected from four OICs of business intelligence and analytics software show that our method outperforms multiple baseline methods based on unsupervised and supervised graph embeddings. This study provides methodological guidance for user segmentation based on structured community data and semantic social relations and provides insights for its practice in heterogeneous OICs.

show abstract

Vicinal Vertex Allocation for Matrix Factorization in Networks

Cited by 10 publications

References 42 publications

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information

A User Segmentation Method in Heterogeneous Open Innovation Communities Based on Multilayer Information Fusion and Attention Mechanisms

Contact Info

Product

Resources

About