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1st International Conference on Informatics and Analytics (ICIA)

作者： Manju, G. Geetha, T., V Coll Engn Guindy Chennai Tamil Nadu India

ISBN: (纸本)9781450347563

Social network like Twitter is used by researchers and academicians to develop their professional relationship and as well it acts as a communication tool to share their research ideas, and research results. Among the enormous number of tweets, certain tweets contain general information and others might contain academic related information that aid scholarly communication. In order to identify such tweets, our work aims to classify the tweets as academic or non-academic tweet based on new combination of tweet content features like hashtags, terms that influence academic domain and user related features like follower/followee graph, mentions and tweet reply status. Spreading activation based label propagation algorithm is used for tweet classification. In order to choose the appropriate random neighbor and effectively propagate the labels, the activation value computed by Spreading activation is used. Further, this helps to reduce the number of iterations and thereby, speeds up the convergence of the label propagation algorithm. The comprehensive experimental results shows that spreading activation based label propagation algorithm effectively classifies the tweets as academic or non-academic using the inferred academic feature combination with marginal decrease in the number of iterations.

关键词： Tweet Classification label propagation algorithm Academic Tweet Twitter Spreading activation algorithm

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Circularly Searching Core Nodes Based label propagation algorithm for Community Detection

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2016年第8期30卷 1659024-1659024页

作者： Shang, Ronghua Zhang, Weitong Jiao, Licheng Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

With the application of community detection in complex networks becoming more and more extensive, the application of more and more algorithms for community detection are proposed and improved. Among these algorithms, the label propagation algorithm is simple, easy to perform and its time complexity is linear, but it has a strong randomness. Small communities in the label propagation process are easy to be swallowed. Therefore, this paper proposes a method to improve the partition results of label propagation algorithm based on the pre-partition by circularly searching core nodes and assigning label for nodes according to similarity of nodes. First, the degree of each node of the network is calculated. We go through the whole network to find the nodes with the maximal degrees in the neighbors as the core nodes. Next, we assign the core nodes' labels to their neighbors according to the similarity between them, which can reduce the randomness of the label propagation algorithm. Then, we arrange the nodes whose labels had not been changed as the new network and find the new core nodes. After that, we update the labels of neighbor nodes according to the similarity between them again until the end of the iteration, to complete the pre-partition. The approach of circularly searching for core nodes increases the diversity of the network partition and prevents the smaller potential communities being swallowed in the process of partition. Then, we implement the label propagation algorithm on the whole network after the pre-partition. Finally, we adopt a modified method based on the degree of membership determined by the bidirectional attraction of nodes and their neighbor communities. This method can reduce the possibility of the error in partition of few nodes. Experiments on artificial and real networks show that the proposed algorithm can accurately divide the network and get higher degree of modularity compared with five existing algorithms.

关键词： Community detection label propagation algorithm core node similarity

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Memory-based label propagation algorithm for community detection in social networks

Memory-based label propagation algorithm for community detec...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Hosseini, Razieh Azmi, Reza Alzahra Univ Dept Comp Engn OSSL Tehran Iran

ISBN: (纸本)9781479988181

Community detection in social network is a significant issue in the study of the structure of a network and understanding its characteristics. A community is a significant structure formed by nodes with more connections between them. In recent years, several algorithms have been presented for community detection in social networks among them label propagation algorithm is one of the fastest algorithms, but due to the randomness of the algorithm its performance is not suitable. In this paper, we propose an improved label propagation algorithm called memory-based label propagation algorithm (MLPA) for finding community structure in social networks. In the proposed algorithm, a simple memory element is designed for each node of graph and this element store the most frequent common adoption of labels iteratively. Our experiments on the standard social network datasets show a relative improvement in comparison with other community detection algorithms.

关键词： label propagation algorithm community detection social networks complex networks

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label propagation algorithm based on edge clustering coefficient for community detection in complex networks

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2014年第30期28卷 1450216-1450216页

作者： Zhang, Xian-Kun Tian, Xue Li, Ya-Nan Song, Chen Tianjin Univ Sci & Technol Coll Comp Sci & Informat Technol Tianjin 300222 Peoples R China

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm.

关键词： Complex network community discovery label propagation algorithm edge clustering coefficient

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

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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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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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Opinion formation over dynamic cluster networks: A multistage opinion dynamics model for large-scale group decision-making

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 275卷

作者： Dong, Jianglin Zhao, Yiyi Mao, Haixia Yang, Junyi Hu, Jiangping Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Yangtze Delta Reg Inst Huzhou Huzhou 313001 Peoples R China Southwestern Univ Finance & Econ Fac Business Adm Sch Business Adm Chengdu 611130 Peoples R China Southwestern Univ Finance & Econ Lab Philosophy & Social Sci Minist Educ Chengdu 611130 Peoples R China

Inspired by the fact that large-scale group decision-making (LSGDM) problems often require clustering and multistage discussions to reach the final decision, we propose a novel multistage opinion dynamics (MSOD) model on a dynamic cluster network, detected using the label propagation algorithm. This model focuses on the co-evolution of opinion dynamics and cluster networks and is divided into four stages: I. unorganized intra-cluster communication, II. cluster leader selection based on historical data, III. inter-cluster leader communication, and IV. intra-cluster organized communication. Cluster leaders are selected in the stage II through a bottom-up election mechanism, and clusters continue to switch between stages III and IV until the system reaches a steady state. The simulation results demonstrate that each cluster continuously reaches a consensus under the guidance of the cluster leader, whereas group opinions converge towards a consensus, independence, or polarization depending on the communication modalities employed by the cluster leaders. Moreover, the transformation of social networks from a connected cluster network to a multiisolated network eventually evolves into a broadcast network with multiple leaders, which has strong implications for public opinion control.

关键词： Large-scale group decision-making Multistage opinion dynamics model Dynamic cluster networks label propagation algorithm Bottom-up election mechanism

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SGCL: Semi-supervised Graph Contrastive Learning with confidence propagation algorithm for node classification

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KNOWLEDGE-BASED SYSTEMS 2024年 301卷

作者： Jiang, Wenhao Bai, Yuebin Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

Semi-Supervised Graph Learning (SSGL) aims to predict massive unknown labels based on a subset of known labels within a graph. Recently, graph neural network, one of the most popular SSGL approaches, has garnered considerable research interest and achieved remarkable progress. However, many of these methods perform unsatisfactorily with limited labeled data. Graph contrastive learning (GCL), which utilizes unlabeled data to generate supervision, partially addresses this issue but does not fully exploit label information. To address this challenge, we propose SSGL algorithm, Semi-supervised Graph Contrastive Learning with Confidence propagation algorithm (SGCL). SGCL comprises two stages of contrastive learning. In the first stage, we employ unsupervised contrastive learning to initialize the model with graph augmentation. In the second stage, in order to fully leverage known labels and graph structure, we incorporate supervised contrastive learning which utilizes supervision signals obtained from confidence propagation algorithm. By combining supervised contrastive learning and unsupervised contrastive learning, the embedding quality and the classification accuracy can be further enhanced. At last, comprehensive experiments demonstrate that SGCL outperforms the best baseline method by an average of 2.23% across six datasets, highlighting the effectiveness of our approach.

关键词： Graph Contrastive Learning label propagation algorithm Node classification Pseudo label

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TLGPA: Two-Level Graph Partitioning algorithm under Multi-Machine Multi-Card System 27

TLGPA: Two-Level Graph Partitioning Algorithm under Multi-Ma...

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27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Cui, Huanqing Lv, Silin Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Recently, the distributed graph processing systems are widely deployed on the cluster of computers with multiple accelerating cards, but as the basis of these systems, the traditional graph partitioning algorithms only partition graphs to computers without considering the distribution over multiple accelerating card. To tackle the graph partitioning requirements on a multi-computer multi-card system, this paper proposes a two-level graph partitioning algorithm (TLGPA). The first level aims to divide the graph to computers based on the piece-wise adaptive mutation differential evolution algorithm (PAMDE), whose purpose is to minimize the communication cost while keeping load balance. The second level aims to divide the sub-graph of a computer to different accelerating cards based on label propagation algorithm (LPA) whose purpose is to keep load balance. Experimental results with real-world graphs demonstrate that the proposed algorithm outperforms the rival algorithms in most cases.

关键词： Graph partition Vertex partitioning Differential evolution algorithm label propagation algorithm

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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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