Semi-supervised community detection based on non-negative matrix factorization with node popularity

Liu, Xiao; Wang, Wenjun; He, Dongxiao; Jiao, Pengfei; Jin, Di; Cannistraci, Carlo Vittorio

doi:10.1016/j.ins.2016.11.028

Cited by 71 publications

(25 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…f This algorithm provides a framework via flattening a multi-dimensional network into a weighted network, and then employs the existing monolayer algorithms for community detection, thereby the complexity is uncertain with the increasing of network scale, global computation becomes time-consuming, which promoting local community detection into our view. Liu et al (2017) proposed an improved multi-objective evolutionary approach for community detection in multilayer networks. Aiming at solving the local community detection problem, they employ a string-based representative scheme and genetic operation and local search.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

100

View full text Add to dashboard Cite

Community detection is one of the most popular researches in a variety of complex systems, ranging from biology to sociology. In recent years, there’s an increasing focus on the rapid development of more complicated networks, namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on a multilayer network without modifications. Thus, in this paper, we offer overall comparisons of existing works and analyze several representative algorithms, providing a comprehensive understanding of community detection methods in multilayer networks. The comparison results indicate that the promoting of algorithm efficiency and the extending for general multilayer networks are also expected in the forthcoming studies.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The rank k corresponds to the number of divided communities. It has been widely utilized in detecting communities in complex networks (Jiao et al 2017;Liu et al 2017;Wu et al 2018). Recently, Ma et al applied this method (S2j-NMF) to community detection for multilayer networks (Ma et al 2018).…”

Section: Nonnegative Matrix Factorization Methodsmentioning

confidence: 99%

A survey of community detection methods in multilayer networks

Huang

Chen

Ren

et al. 2020

Data Min Knowl Disc

100

View full text Add to dashboard Cite

show abstract

“…In this study, we identify the lncRNA, miRNA and mRNA-associated regulatory modules by a non-negative matrix factorization (NMF)-based framework. The corresponding objective function of standard NMF [31, 33] is formulated as follows:where ||.|| F denotes the Frobenius norm.…”

Section: Methodsmentioning

confidence: 99%

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

et al. 2019

View full text Add to dashboard Cite

BackgroundNon-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood.ResultsHere we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem.ConclusionsWe applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2654-3) contains supplementary material, which is available to authorized users.

show abstract

“…For example, the result of SC3 depends highly on the input setting of cluster number. However, in practical scientific research, the number of optional clusters is usually unknown before the simulation (Deng et al, 2011 ; Liu et al, 2017 ). Thus, it is a big challenge to determine the cluster number when there is no prior information about cell types.…”

Section: Introductionmentioning

confidence: 99%

EC-PGMGR: Ensemble Clustering Based on Probability Graphical Model With Graph Regularization for Single-Cell RNA-seq Data

Zhu

Zhang

et al. 2020

Front. Genet.

View full text Add to dashboard Cite

Advances in technology have made it convenient to obtain a large amount of s ingle c ell RNA seq uencing (scRNA-seq) data. Since that clustering is a very important step in identifying or defining cellular phenotypes, many clustering approaches have been developed recently for these applications. The general methods can be roughly divided into normal clustering methods and integrated (ensemble) clustering methods which combine more than two normal clustering methods aiming to get much more informative performance. In order to make a contrast with the integrated clustering algorithm, the normal clustering method is often called individual or base clustering method. Note that the results of many individual clustering methods are often developed to capture one aspect of the data, and the results depend on the initial parameter settings, such as cluster number, distance metric and so on. Compared with individual clustering, although integrative clustering method may get much more accurate performance, the results depend on the base clustering results and integrated systems are often not self-regulation. Therefore, how to design a robust unsupervised clustering method is still a challenge. In order to tackle above limitations, we propose a novel E nsemble C lustering algorithm based on P robability G raphical M odel with G raph R egularization, which is called EC-PGMGR for short. On one hand, we use parameter controlling in P robability G raphical M odel (PGM) to automatically determine the cluster number without prior knowledge. On the other hand, we add a regularization term to reduce the effect deriving from some weak base clustering results. Particularly, the integrative results collected from base clustering methods can be assembled in the form of combination with self-regulation weights through a pre-learning process, which can efficiently enhance the effect of active clustering methods while weaken the effect of inactive clustering methods. Experiments are carried out on 7 data sets generated by different platforms with the number of single cells from 822 to 5,132. Results show that EC-PGMGR performs better than 4 alternative individual clustering methods and 2 ensemble methods in terms of accuracy including A djusted R and I ndex (ARI) and N ormalized M utual I nformation (NMI), robustness, effectiveness and so on. EC-PGMGR provides an effective way to integrate different clustering results for more accurate and reliable results in further biological analysis as well. It may provide some new insights to the other applications of clustering.

show abstract

Semi-supervised community detection based on non-negative matrix factorization with node popularity

Cited by 71 publications

References 37 publications

A survey of community detection methods in multilayer networks

A survey of community detection methods in multilayer networks

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

EC-PGMGR: Ensemble Clustering Based on Probability Graphical Model With Graph Regularization for Single-Cell RNA-seq Data

Contact Info

Product

Resources

About