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label propagation algorithm for community discovery based on centrality and common neighbours

JOURNAL OF SUPERCOMPUTING 2024年第8期80卷 11816-11842页

作者： Douadi, Asma Kamel, Nadjet Sais, Lakhdar Univ Ferhat Abbas Setif 1 Dept Comp Sci LRSD Setif Algeria Univ Artois CRIL CNRS Lens France

We propose a label propagation-based algorithm to extract community structure using a new similarity measure based on centrality and common neighbours. Initially, a distinct label is assigned to each vertex. Along the process, each vertex adopts the label that most of its similar neighbours have. Communities are defined when closely related groups of vertices agree on a single label. Experiments on both real-world and synthetic networks show that our algorithm has better stability than the label propagation algorithm and finds communities with small sizes and indistinct boundaries more effectively than most state-of-the-art community detection methods.

关键词： label propagation algorithm Centrality Common neighbours Community structure

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Khmer Sentiment Lexicon Based on PU Learning and label propagation algorithm

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2023年第3期22卷 1-18页

作者： Li, Chao Yan, Xin Xu, Guangyi Deng, Zhongying Mo, Yuanyuan Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

The sentiment lexicon is an important tool for natural language processing tasks. In addition to being able to determine the sentiment polarity of words or phrases, it can assist attribute-level, sentence-level, and text-level sentiment analysis tasks. In light of the fact that tagging data and corpora for the Khmer language are scarce, where most resources related to sentiment lexicons are for English, this paper proposes a method for constructing a sentiment lexicon for Khmer based on Positive-Unlabeled learning (PU Learning) and the label propagation algorithm. Sentiment words are first extracted from a corpus using the Spy technique of PU learning method. The main idea is to purify the set of N-class examples, train the MLP classifier, and continuously delete spy words and increase the number of P-class words in the iterative process. Following this, the sentiment polarity of the candidate words is determined. By considering the problem of determining the sentiment polarity of the candidate words as one of calculating its probability distribution, a small number of labeled sentiment words and candidate words are used to construct a graph model. The contextual information of the candidate words is used to construct a simple supplementary graphmodel of the set of sentiment words through word co-occurrence and triangulation, where this enhances the correlation between data items. The sentiment polarity of the candidate words is then determined through the label propagation algorithm. The results of experiments show that the proposed method can be used to construct a Khmer sentiment lexicon with a small number of labeled data and a small corpus without requiring excessive manual labeling.

关键词： Khmer sentiment lexicon PU learning label propagation algorithm graph model Spy technique

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An Improved label propagation algorithm for Undirected Weighted Networks 12

An Improved Label Propagation Algorithm for Undirected Weigh...

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12th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)

作者： Li, Peilei Yu, Hanging Zhang, Yijin Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

ISBN: (纸本)9798350374377

In recent years, with the rapid development of network technology, the scale of social networks has become increasingly large and diversified. The detection of community has become a hot spot in the research of social network. label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity. However, the randomness of LPA affects the accuracy and stability of the community. This paper proposes an improved label propagation algorithm for undirected weighted networks. In the first stage, considering both node importance and local topological information, an improved LPA is designed to divide the undirected weighted network into initial communities, thereby reducing the instability of LPA. In the second stage, a strategy for increasing modularity is designed to merge small communities, thereby improving the quality of community partitioning. Experimental results show that the proposed community detection algorithm has better community detection quality compared to LPA and four classical community detection algorithms.

关键词： Community detection LeaderRank algorithm label propagation algorithm

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Community detection using constrained label propagation algorithm with nodes exemption

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COMPUTING 2022年第2期104卷 339-358页

作者： Chin, Jia Hou Ratnavelu, Kuru UCSI Univ Fac Business & Management Kuala Lumpur 56000 Malaysia UCSI Univ Inst Comp Sci & Digital Innovat Kuala Lumpur 56000 Malaysia Univ Malaya Inst Math Sci Kuala Lumpur 50603 Malaysia

The detection of community structure plays an important role in understanding the properties and characteristics of complex networks. The label propagation algorithm (LPA) emerges as a popular community detection method, due to its simplicity and low computational cost. Nonetheless, the LPA is not without its limitations so that the Semi Synchronous Constrained label propagation algorithm (SSCLPA) is a modified LPA that implements various constraints to ameliorate the stability of the LPA. Aside from giving accurate and deterministic detection, it can avoid trivial detection. In this paper the SSCLPA is extended into weighted and directed networks, so that nodes which fulfill certain conditions are updated separately at the end of the algorithm. Furthermore, some modifications are performed on the propagation processes in the SSCLPA. These new features and modifications improve the time efficiency of the SSCLPA with only marginal loss in the quality of the detection. Our proposed method is tested and compared to the other community detection methods in various benchmark and real-world networks. The results showed that the proposed method is a well-balanced method with features that takes into account the stability, quality and time efficiency of the detection.

关键词： Complex networks Community structure Community detection label propagation algorithm

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Graph semi-supervised soft sensor modeling based on label propagation algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第12期35卷 125104-125104页

作者： Gao, Shiwei Li, Tianzhen Dong, Xiaohui Northwest Normal Univ Sch Comp Sci & Engn Lanzhou 730070 Peoples R China

Data-driven soft sensor modeling methods have become prevalent in the industry. Nonetheless, the complexity of industrial processes often leads to the absence or difficulty in obtaining key labeled data, and existing methods frequently fail to fully utilize the inherent correlations between variables. This paper proposes a novel graph semi-supervised soft sensor modeling method using the label propagation algorithm to address these issues. This method utilizes correlations within the data to assign pseudo-labels to unlabeled data reasonably and employs graph convolutional networks to capture spatial relationships between nodes. Additionally, by embedding a long short-term memory structure, the model can capture temporal dependencies of the data while focusing on spatial structures. Furthermore, the introduction of a residual structure enables the model to directly learn the differences between inputs and outputs, facilitating information transmission, and improving the model's feature extraction ability. Experiments demonstrate the effectiveness of the method.

关键词： soft sensor semi-supervised learning graph convolutional network label propagation algorithm LSTM

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FLPA: A fast label propagation algorithm for detecting overlapping community structure

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期234卷

作者： Yan, Rong Yuan, Wei Su, Xiangdong Zhang, Ziyi Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Inner Mongolia Key Lab Mongolian Informat Proc Tec Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Inform Hohhot Peoples R China

In complex networks, there are a huge number of overlapping community structures, and those structures have been an increasing concern in recent years. The label propagation algorithm (LPA) family is a kind of mainstream method that discovers different community structures in complex networks. But these methods often struggle with stability and feasibility, as well as issues sensitive to network size. In this paper, to solve the above problems and improve the detection accuracy, we present a fast label propagation algorithm (FLPA) based on node influence and label weight. In FLPA, we first use the graph compression technique to reduce the network size. Then, we propose a new node influence calculation method and fuse it with the ��-path similarity to precisely control the label weight in the label propagation stage. Through the above process, FLPA makes the labels that are propagated and received by nodes more reasonable and further improves the detection accuracy. Finally, we restore the network and make the compressed node belong to the same community as its corresponding super node. Experimental results on 10 real and 58 synthetic networks indicate that FLPA is suitable for detecting overlapping community structures regardless of network scale and accomplishes better than state-of-the-art methods on stability and feasibility.

关键词： Complex network Community detection label propagation algorithm Overlapping community detection

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A low complexity nonlinearity impairment compensation scheme assisted by label propagation algorithm

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OPTICS COMMUNICATIONS 2022年 506卷 127547-127547页

作者： Li, Baokun Bai, Chenglin Xu, Hengying Yang, Lishan Sun, Weibin Yu, Xinkuo Zhao, Ruqing Zhou, Tanglei Ge, Peiyun Lv, Xiuhua Luo, Xueyuan Liaocheng Univ Sch Phys Sci & Informat Engn Liaocheng 252000 Shandong Peoples R China Shandong Prov Key Lab Opt Commun Sci & Technol Liaocheng 252000 Shandong Peoples R China

In this paper, we firstly applied label propagation algorithm (LPA) to coherent optical communication system for nonlinear impairment compensation (NLC), which could exploit the relationship among constellation points to segment the community with only very small amount of label data to predict the label situation on most test data. The effectiveness of the proposed scheme was verified through 28 GBaud polarization division multiplexing (PDM)-16 quadrature amplitude modulation (QAM), PDM-32QAM and PDM-64QAM simulation system. By analyzing the simulation results, we obtained the best propagation coefficient alpha, which demonstrated that the proposed scheme could effectively make nonlinear decision on received symbols and compensate signal impairment caused by Kerr nonlinear effect and amplified spontaneous emission (ASE) noise, showing a strong robustness and generalization to variations of the transmission environment under different modulation formats, transmission distances, launch power and channel numbers. Then, the proposed scheme was further studied by 20 GBaud PDM-16QAM long-haul transmission experiments. The results demonstrated that the proposed scheme required no training process and exhibited optimistic compensation performance to the coherent optical communication system with nonlinear impairments. Finally, we analyzed computational complexity of the LPA-assisted NLC scheme in detail, which was O(m*N).

关键词： Coherent optical communication Nonlinear impairment compensation label propagation algorithm Single channel Wavelength division multiplexing Polarization division multiplexing

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Graph-based label propagation algorithm for community detection

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2020年第6期11卷 1319-1329页

作者： Yang, Gui Zheng, Wenping Che, Chenhao Wang, Wenjian Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China Minist Educ Key Lab Computat Intelligence & Chinese Informat Taiyuan 030006 Shanxi Peoples R China

Community detection is one of the most important topics in complex network analysis. Among a variety of approaches for detecting communities, the label propagation algorithm (LPA) is the simplest and time-efficient approach. However, the original label propagation algorithm is not stable due to the randomness in its propagation process. In this paper, we propose a graph-based label propagation algorithm (GLPA) to detect communities incorporating the node similarity and connectivity information during the propagation of the labels. First, we define node similarity between adjacent nodes, and change each node's label to that of its most similar neighbor node. Based on the label propagation process, GLPA constructs a label propagation graph to get candidate communities. Then, GLPA calculates the connected components of the label propagation graph. Each connected component is treated as a candidate community in the next step. Second, GLPA constructs a weighted graph to obtain final communities, in which each connected component are treated as a super-node, and the number of edges lying between the corresponding components as the weight of edges. We compute the merging factor for each node in the weighted graph and merge super nodes with higher merging factor to its most similar node iteratively to reach the maximum complementary entropy. Compared with 8 other classical community detection algorithms on LFR artificial networks and 12 real world networks, the proposed algorithm GLPA shows preferable performance on stability, NMI, ARI, modularity.

关键词： Complex network Community detection label propagation algorithm

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An Improved label propagation algorithm-Based Method to Develop Sectionalizing Strategies for Parallel Power System Restoration

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IEEE ACCESS 2020年 8卷 118497-118509页

作者： Huang, Shujian Li, Changcheng Li, Zhicheng Xu, Wenwen Yang, Long Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China State Grid Fujian Elect Power Co Ltd Elect Power Res Inst Fuzhou 350007 Peoples R China

The first task of parallel power system restoration is to sectionalize the blackout system into multiple subsystems. This paper applies the complex network community discovery theory into this sectionalizing problem and proposes an improved label propagation algorithm-based sectionalizing method considering the system topology and operation before blackouts. Firstly, each blackstart (BS) unit bus is marked with a different subsystem label. A label propagation matrix is calculated based on the active power of branches before the blackout. Then, to avoid the label oscillation, a novel label impact strategy considering the influence of the bus label itself is developed to improve the label propagation matrix. The buses' labels propagate to neighboring buses as the improved label propagation matrix until they do not change. The initial sectionalizing strategy is obtained through classifying the buses with the same label in the same subsystem. Finally, the sectionalizing constraints are used to evaluate the feasibility of the initial strategy. For the initial strategy that does not satisfy the constraints, a refining method to minimize the absolute value of active power exchange among subsystems is proposed to determine the final sectionalizing strategy based on it. Case studies on IEEE 39-bus and IEEE 118-bus test systems verify the effectiveness of the proposed method. The results indicate that the proposed method has advantages in creating strongly connected subsystems.

关键词： Power system restoration sectionalizing strategy label propagation algorithm tie line

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New label propagation algorithm with pairwise constraints

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PATTERN RECOGNITION 2020年 106卷 107411-107411页

作者： Bai, Liang Wang, Junbin Liang, Jiye Du, Hangyuan Shanxi Univ Inst Intelligent Informat Proc Taiyuan 030006 Shanxi Peoples R China Shanxi Univ Sch Comp & Informat Technol Taiyuan 030006 Shanxi Peoples R China

The label propagation algorithm is a well-known semi-supervised clustering method, which uses pre-given partial labels as constraints to predict the labels of unlabeled data. However, the algorithm has the following limitations: (1) it does not fully consider the misalignment between the pre-given labels and clustering labels, and (2) it only uses label information as clustering constraints. Real applications not only contain partial label information but pairwise constraints on a dataset. To overcome these deficiencies, a new version of the label propagation algorithm is proposed, which makes use of pairwise relations of labels as constraints to construct an optimization model for spreading labels. Experimental analysis was used to compare the proposed algorithm with 8 other semi-supervised clustering algorithms on 11 benchmark datasets. The experimental results demonstrated that the proposed algorithm is more effective than other algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Cluster analysis Semi-supervised clustering label propagation algorithm Pairwise constraint Spectral clustering

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