Graph-Based Transductive Label Propagation Approach for Multi-label Classification

Abstract Background Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs’ nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. Results We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs’ weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Conclusions Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.

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Towards Co-Evolution of Random-Walk-Based Embedding and Label Propagation for Node Classification

2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) ◽

10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00228 ◽

2020 ◽

Author(s):

Ruixuan Zhao ◽

Shenghang Liu ◽

Wei Rao ◽

Hao Xu ◽

Chongning Na ◽

...

Keyword(s):

Random Walk ◽

Label Propagation ◽

Node Classification

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Community Detection by Motif-Aware Label Propagation

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3378537 ◽

2020 ◽

Vol 14 (2) ◽

pp. 1-19 ◽

Cited By ~ 2

Author(s):

Pei-Zhen Li ◽

Ling Huang ◽

Chang-Dong Wang ◽

Jian-Huang Lai ◽

Dong Huang

Keyword(s):

Community Detection ◽

Label Propagation

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LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Entropy ◽

10.3390/e23050497 ◽

2021 ◽

Vol 23 (5) ◽

pp. 497

Author(s):

Huan Li ◽

Ruisheng Zhang ◽

Zhili Zhao ◽

Xin Liu

Keyword(s):

Community Detection ◽

Real World ◽

Label Propagation ◽

Node Importance ◽

Propagation Algorithm ◽

Synthetic Networks

Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

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