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

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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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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Community detection with the label propagation algorithm: A survey

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2019年 534卷 122058-000页

作者： Garza, Sara E. Elisa Schaeffer, Satu Univ Autonoma Nuevo Leon Fac Ingn Mecan & Elect San Nicolas De Los Garza Mexico

Community detection aims at discovering the structure, behavior, dynamics, and organization of a complex network by finding cohesive groups where nodes (entities) are, in some sense, more similar within the group and groups are in some fashion separated from the other groups. The label propagation algorithm (LPA), which mimics epidemic contagion by spreading labels, is a popular algorithm for this task. Its variants (enhancements, extensions, combinations) seek to improve or to adapt it while preserving the advantages of the original formulation. This survey gathers, classifies, and summarizes LPA-related advances from 2007 to mid 2019. We also experiment with combinations of numerous LPA "building blocks" on two types of small synthetic networks;essentially all of the 13,834 resulting LPA variants have their niche in terms of either higher solution quality or lower computational load with acceptable quality, but none is universally superior. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Community detection Graph clustering label propagation algorithm LPA

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Community detection using a potential-based label propagation algorithm

Community detection using a potential-based label propagatio...

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2013 International Conference on Communication Technology (ICCT 2013)

作者： Lixin Shi Junxing Zhang College of Information and Communication Engineering Dalian Nationalities University

Due to nearly linear time complexity, label propagation algorithm(LPA) can be applied to large real-world networks with millions of nodes. Nevertheless, because of introduction of randomness, the robustness of algorithm is severely hampered. To reduce the randomness, a potential-based label propagation algorithm(LPAP) is proposed, which only initializes the node with extreme potential and updates the label according to the sum potential value of its neighbors. The experimental results show that LPAP is significantly more robust than LPA and is suitable for large network too.

关键词： community detection label propagation algorithm data field potential function robustness

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label propagation Based Community Detection algorithm with Dpark 1

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4th International Conference on Computational Social Network (CSoNet)

作者： Wang, Ting Qian, Xu Wang, Xiaomeng China Univ Min & Technol Sch Mech Elect & Informat Engn Beijing Peoples R China

ISBN: (数字)9783319217864

ISBN: (纸本)9783319217864;9783319217857

Numerous methods for detecting communities on social networks have been proposed in recent years. However, the performance and scalability of the algorithms are not enough to work on the real-world large-scale social networks. In this paper, we propose Improved Speaker-listener label propagation algorithm (iSLPA), an efficient and fully distributed method for community detection. It is implemented with Dpark, which is a Python version of Spark and a lightning-fast cluster computing framework. To the best of our knowledge, this is the first attempt at community detection on Dpark. It can automatically work on three kinds of networks: directed networks, undirected networks, and especially bipartite networks. In iSLPA, we propose a new initialization and updating strategy to improve the quality and scalability for detecting communities. And we conduct our experiments on real-world social networks datasets on both benchmark networks and Douban (http://***) user datasets. Experimental results demonstrate that iSLPA has a comparable performance than SLPA, and have confirmed our algorithms is very efficient and effective on the overlapping community detection of large-scale networks.

关键词： Community detection label propagation algorithm Dpark iSLPA

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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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Community Discovering algorithm based on Nodes with Maximum Degree and label propagation

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INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING 2016年第12期9卷 115-126页

作者： Li Shengli Chen Deyun Yao Yuanzhe Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China

label propagation algorithm (LPA algorithm) is gotten widely attention by its simplicity, rapidity and greater effectiveness. Aimed at the problem of label updating process is sensitive to the order of nodes in LPA algorithm, an improved algorithm is proposed in this paper. The algorithm starts from the nodes with maximum degree in the network, according to its neighbor nodes' community similarity index to decide whether its label setting, and then completes label setting in the first round;On this basis, it continues to carry out the iteration in label propagation algorithm, completes the entire network of community structure detection. The experimental results show that the improved algorithm is slightly better than the original algorithm at running time and number of iterations, has higher robustness, prevents the occurrence of the trivial solution.

关键词： Community Detection label propagation algorithm Nodes with Maximum degree Neighbour Node

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Detecting community structure using label propagation with consensus weight in complex network

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Chinese Physics B 2014年第9期23卷 594-601页

作者：梁宗文李建平杨帆 Athina Petropulu School of Computer Science and Engineering University of Electronic Science and Technology of China Electrical and Computer Engineering Department RutgersThe State University of New Jersey

Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight （LPAcw）, and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions.

关键词： label propagation algorithm community detection consensus cluster complex network

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LPANNI: Overlapping Community Detection Using label propagation in Large-Scale Complex Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第9期31卷 1736-1749页

作者： Lu, Meilian Zhang, Zhenglin Qu, Zhihe Kang, Yu Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Overlapping community structure is a significant feature of large-scale complex networks. Some existing community detection algorithms cannot be applied to large-scale complex networks due to their high time or space complexity. label propagation algorithms were proposed for detecting communities in large-scale networks because of their linear time complexity, however most of which can only detect non-overlapping communities, or the results are inaccurate and unstable. Aimed at the defects, we proposed an improved overlapping community detection algorithm, LPANNI (label propagation algorithm with Neighbor Node Influence), which detects overlapping community structures by adopting fixed label propagation sequence based on the ascending order of node importance and label update strategy based on neighbor node influence and historical label preferred strategy. Extensive experimental results in both real networks and synthetic networks show that, LPANNI can significantly improve the accuracy and stability of community detection algorithms based on label propagation in large-scale complex networks. Meanwhile, LPANNI can detect overlapping community structures in large-scale complex networks under linear time complexity.

关键词： label propagation algorithm large-scale network neighbor node influence overlapping community detection

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