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label propagation Techniques for Artifact Detection in Imbalanced Classes Using Photoplethysmogram Signals

IEEE ACCESS 2024年 12卷 81221-81235页

作者： Macabiau, Clara Le, Thanh-Dung Albert, Kevin Shahriari, Mana Jouvet, Philippe Noumeir, Rita Ecole Technol Super Biomed Informat Proc Lab Montreal PQ H3C 1K3 Canada Univ Montreal CHU St Justine Res Ctr CHU St Justine Hosp Montreal PQ H3T 1J4 Canada

This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised label propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the "artifacts" class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.

关键词： Motion artifacts imbalanced classes label propagation algorithm machine learning classifiers photoplethysmogram (PPG) signals

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Weighted label propagation based on Local Edge Betweenness

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JOURNAL OF SUPERCOMPUTING 2019年第12期75卷 8094-8114页

作者： Joghan, Hamid Shahrivari Bagheri, Alireza Azad, Meysam Amirkabir Univ Technol 350 Hafez AveValiasr Sq Tehran Iran

In complex networks, especially social networks, networks could be divided into disjoint partitions. However, nodes could be partitioned such that the number of internal edges (the edges between the vertices within the same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting community structure helps data scientists to extract meaningful information from networks and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. Yet, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. label propagation algorithm (LPA), a fast random-based algorithm, can easily detect communities in big graphs, but its accuracy compared to other algorithms is low. In this paper, we have improved LPA accuracy, and to achieve this goal, we propagate label based on the Local Edge Betweenness score. The proposed algorithm, named Weighted label propagation based on Local Edge Betweenness, is able to identify distinct communities in both the real-world and artificial networks. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable;additionally, the proposed algorithm is scalable.

关键词： Complex networks Community detection label propagation algorithm Edge betweenness Weighted graph Scalability

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Detecting Overlapping Communities in Complex Networks: An Evolutionary label propagation Approach

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第1期23卷 327-360页

作者： Saif, Mojtaba Samie, Mohammad Ebrahim Hamzeh, Ali Shiraz Univ CSE & IT Dept Shiraz Fars Iran Jahrom Univ Dept Comp Engn & IT Jahrom Fars Iran

A challenging issue in complex network analysis is overlapping community detection, which has attracted many studies. label propagation algorithm (LPA) is one of the famous studies to detect communities. But it has some weaknesses such as using local information and randomly choosing the sequences of processing nodes. We introduce Evolutionary label propagation algorithm (ELPA) to solve these problems and improve accuracy. ELPA uses an intelligent search instead of randomly processing nodes and fuses local and global perspectives. The proposed ELPA is compared with several state-of-the-art algorithms on synthetic and real-world networks with different sizes, densities, and complexities. The results indicate that ELPA provides better results on most of the test instances. Therefore, ELPA is an accurate and efficient algorithm for detecting overlapping communities.

关键词： Complex network label propagation algorithm overlapping community detection

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Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network

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JOURNAL OF THEORETICAL BIOLOGY 2017年 430卷 9-20页

作者： Wen, Yu-Ting Lei, Hai-Jun You, Zhu-Hong Lei, Bai-Ying Chen, Xing Li, Li-Ping Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Guangdong Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Shenzhen Univ Sch Biomed Engn Shenzhen 518060 Guangdong Peoples R China Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen 518060 Guangdong Peoples R China Guangdong Key Lab Biomed Measurements & Ultrasoun Shenzhen 518060 Guangdong Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 21116 Jiangsu Peoples R China

Prediction of protein-protein interactions (PPIs) is of great significance. To achieve this, we propose a novel computational method for PPIs prediction based on a similarity network fusion (SNF) model for integrating the physical and chemical properties of proteins. Specifically, the physical and chemical properties of protein are the protein amino acid mutation rate and its hydrophobicity, respectively. The amino acid mutation rate is extracted using a BLOSUM62 matrix, which puts the protein sequence into block substitution matrix. The SNF model is exploited to fuse protein physical and chemical features of multiple data by iteratively updating each original network. Finally, the complementary features from the fused network are fed into a label propagation algorithm (LPA) for PPIs prediction. The experimental results show that the proposed method achieves promising performance and outperforms the traditional methods for the public dataset of H. pylori, Human, and Yeast. In addition, our proposed method achieves average accuracy of 76.65%, 81.98%, 84.56%, 84.01% and 84.38% on E. coli, C. elegans, H. sapien, H. pylori and M. musculus datasets, respectively. Comparison results demonstrate that the proposed method is very promising and provides a cost-effective alternative for predicting PPIs. The source code and all datasets are available at http://*** dF7rp7N. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Protein-protein interactions Prediction Similarity network fusion label propagation algorithm

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Optimized Parallel label propagation based Community Detection on the Intel® Xeon Phi™ Architecture 27

Optimized Parallel Label Propagation based Community Detecti...

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IEEE 27th International Symposium on Computer Architecture and High Performance Computing

作者： Khlopotine, Andrei B. Sathanur, Arun V. Jandhyala, Vikram Dept Elect Engn Seattle WA 98195 USA

ISBN: (纸本)9781467380119

Complex systems such as social, biological and information networks, characterized by millions to billions of sub-entities and relationships between them are best represented by graphs. A distinguishing feature of such complex systems is the self-organization into dense clusters called communities. Detecting such communities in massive graphs is critical to the understanding of such complex systems. However community detection is non-trivial and is expensive due to a vast number of computations needed to fully realize the underlying relationships. While a number of near-exact and heuristic algorithms is available for community detection, parallelizing such algorithms to fully leverage the advantages of parallel hardware is still a challenging problem. Most presently available approaches attempt to optimize run-time complexities by scaling original serial community detection algorithms. Graph algorithms in general and existing community detection algorithms in particular are known for not being commensurate with linear scalability on parallel systems. Therefore, there is a need for a combination of high-performance software and a hardware platform that would support efficient parallel graph processing with respect to community detection. We present an Intel (R) Xeon Phi (TM) label propagation algorithm (PLPA) variant of community detection algorithm based on label propagation (LP). Our algorithm was tuned for the Intel (R) Xeon Phi (TM) platform a novel architecture that provides 50+ physical cores with simultaneous multithreading. We outline Phi (TM) architecture advantages and limitations for PLPA and massive graph processing in general. We present test results of running our algorithm on large real-world networks while achieving near linear speedups and improving the quality of the detected communities. We also analyze possibilities of processing massive networks that cannot fully fit in a Phi (TM) memory and hence we extend our initial solution to a modified PLPA

关键词： Social Networks label propagation algorithm Parallel Computing Many Integrated Core Architecture

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Social media analysis by innovative hybrid algorithms with label propagation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期210卷

作者： Altinel, Ayse Berna Marmara Univ Fac Technol Dept Comp Engn Istanbul Turkey

Due to the huge size of the data accumulated on microblogging sites, recently, two fundamental questions have become very popular: 1) What percentage of this accumulated data has positive or negative sentiment polarity? 2) How is the distribution of this accumulated data on different topics? Inspired by these motivated necessities, this paper presents several different algorithms which are based on the label propagation algorithm (LPA) in order to handle previously mentioned two fundamentals tasks: sentiment polarity detection task and topic-based text classification task. These algorithms are the label Propagated-Relevance Frequency Classifier (LP-RFC) and LP-Abstract Frequency Classifier (LP-AFC). These algorithms can be defined as new semantic smoothing classi-fiers, which take advantage of the semantic connections among terms in the label propagation phase of the LPA. Additionally, another classifier, namely LP-ComRFC+AFC, was built. LP-ComRFC+AFC is actually a weighted sum-mation classifier of the individual LP-RFC and LP-AFC. Furthermore, considering the shortage of labeled data in real-world scenarios, a semi-supervised version of LP-RFC and LP-AFC, namely "Merging Unlabeled and labeled Instances with Semantic Values of Terms" (MULIS), was designed and implemented. For the experiments of the sentiment polarity detection task, three different datasets were use and for the experiments of topic-based text classification task, a self-collected tweet dataset was use. According to the experimental results, the suggested algorithms, and their composite form, LP-ComRFC+AFC, generated higher F1 scores than all of the baseline al-gorithms at nearly all of the training splits on the datasets.

关键词： label propagation algorithm Social media analysis Topic -based tweet classification Sentiment polarity detection

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Combining Graph Convolutional Neural Networks and label propagation

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2022年第4期40卷 73-73页

作者： Wang, Hongwei Leskovec, Jure Stanford Univ Comp Sci Dept Stanford CA 94305 USA

label propagation algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relationship between LPA and GCN has not yet been systematically investigated. Moreover, it is unclear how LPA and GCN can be combined under a unified framework to improve the performance. Here we study the relationship between LPA and GCN in terms of feature/label Influence, in which we characterize how much the initial feature/label of one node influences the final feature/label of another node in GCN/LPA. Based on our theoretical analysis, we propose an end-to-end model that combines GCN and LPA. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved performance. Our model can also be seen as learning the weights of edges based on node labels, which is more direct and efficient than existing feature-based attention models or topology-based diffusion models. In a number of experiments for semi-supervised node classification and knowledge-graph-aware recommendation, our model shows superiority over state-of-the-art baselines.

关键词： Graph convolutional neural networks label propagation algorithm semi-supervised learning

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Combine labeled and unlabeled data for immune detector training with label propagation

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KNOWLEDGE-BASED SYSTEMS 2022年第0期236卷 107661-107661页

作者： Chen Wen Wang Changzhi Sichuan Univ Sch Cyber Sci & Engn Chengdu Peoples R China Zhihuiyuntian Inc Artificial Intelligence Ctr Chengdu Peoples R China

Artificial immune detectors are the basic recognition components of immune systems. Traditionally, the candidate non-self detectors are compared with the whole self training set to eliminate self reactive ones in negative selection algorithms (NSAs). However, the training process has low efficiency due to the exhausting comparisons. Furthermore, it can be more efficient if we straightforwardly generate self-detectors based on the available self samples to avoid the overwhelmed comparisons. In the paper, a new detector training algorithm is proposed. Firstly, the self training set is enlarged by the label propagation algorithm (LPA) using both labeled and unlabeled samples;and then the newly labeled samples is evaluated based on noisy learning theory to remove the unqualified ones. Finally self-detectors are directly generated at the locations of self samples. The theoretical analysis demonstrated that the time complexity of our algorithm is much reduced, especially that the exponential relationship between self size and time complexity in traditional NSAs is eliminated. The experimental results showed that: not only the time cost of detector training, but also the detection accuracy is improved. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Artificial immune systems Negative selection algorithm Positive selection algorithm Noisy learning label propagation algorithm

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A label propagation approach based on local optimization

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2018年第6期29卷

作者： Zhang, Xiaohong Jiang, Yulin Ren, Jianji Tang, Chaosheng Henan Polytech Univ Coll Comp Sci & Technol Jiaozuo 454000 Peoples R China

Community detection offers an important way to understand the structures and functions of social network. The label propagation algorithm has attracted vast attention since it is very suitable for discovering communities from large-scale networks. However, the algorithm suffers from the instability and ineffciency problem caused by the random policies it adopted. In this paper, we propose a novel label propagation approach based on local optimization to deal with the problem. The approach introduces a pre-propagation mechanism to optimize randomly initialized labels according to special factors, for example, node compactness. After that, it traverses and relabels nodes in the descending order of aggregate influence. The experiment results demonstrate the usefulness and effectiveness of our approach.

关键词： Community detection label propagation algorithm social network pre-propagation

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Quantitative function and algorithm for community detection in bipartite networks

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INFORMATION SCIENCES 2016年 367卷 874-889页

作者： Li, Zhenping Wang, Rui-Sheng Zhang, Shihua Zhang, Xiang-Sun Beijing Wuzi Univ Sch Informat Beijing Peoples R China Harvard Med Sch Brigham & Womens Hosp Dept Med Boston MA USA Acad Math & Syst Sci CAS Natl Ctr Math & Interdisciplinary Sci Beijing Peoples R China

Community detection in complex networks is a topic of high interest in many scientific fields. A bipartite network is a special type of complex network whose nodes are decomposed into two disjoint sets such that no two nodes within the same set are adjacent. Many relationships in real-world systems can be represented by a bipartite network, such as predator-prey relationships, plant-pollinator interactions, and drug-target interactions. While community detection in unipartite networks has been extensively studied in the past decade, identification of modules or communities in bipartite networks is still in its early stage. Several quantitative functions have been developed for evaluating the quality of bipartite network divisions, however, these functions were designed based on null model comparisons and thus are subject to certain resolution limits. In this paper, we propose a new quantitative function called bipartite partition density for community detection in bipartite networks, and use some network examples to demonstrate that this quantitative function is superior to the widely used Barber's bipartite modularity and other functions. Based on the bipartite partition density, the bipartite network community detection problem is formulated into an integer nonlinear programming model in which a bipartite network can be partitioned into reasonable overlapping communities by maximizing the quantitative function. We further develop a heuristic adapted label propagation algorithm (BiLPA) to optimize the bipartite partition density in large-scale bipartite networks. BiLPA is efficient and does not require any prior knowledge about the number of communities in the networks. We conduct extensive experiments on simulated and real-world networks and demonstrate that BiLPA can successfully identify the community structures of bipartite networks. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Bipartite network Community Quantitative function Integer programming label propagation algorithm

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