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Combining Graph Convolutional Neural Networks and label propagation

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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Graph Neural Network with Neighborhood Reconnection 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Guo, Mengying Sun, Zhenyu Wang, Yuyi Liu, Xingwu Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China CRRC Zhuzhou Inst Zhuzhou Peoples R China Dalian Univ Technol Sch Math Sci Dalian Peoples R China

ISBN: (数字)9783031402838

ISBN: (纸本)9783031402821;9783031402838

Graph Neural Network (GNN) models have become increasingly popular for network analysis, especially in node classification tasks. However, the effectiveness of GNNs is compromised by two limitations. First, they implicitly assume that networks are homophilous, leading to decreased performance on heterophilous or random networks commonly found in the real world. Second, they tend to ignore the known node labels, inferring node labels merely from the node features and network structure. This is mainly rooted in the non-uniformity of node degrees, which makes it hard to directly aggregate label information. Hence, we propose a novel framework NRGNN, short for Graph Neural Network with Neighborhood Reconnection. NRGNN adjusts the network structure to increase homophily and uniformize node degrees. Then it applies message-passing-based or PageRank-based GNNs to the reconstructed networks, addressing the two limitations of GNNs. We evaluate NRGNN against 14 state-of-the-art baselines for node classification tasks. The empirical results demonstrate that NRGNN outperforms almost all the baselines, regardless of whether the datasets are homophilous, heterophilous, or random. The techniques might be adapted to more network analysis tasks.

关键词： Graph Neural Network Heterophilous Graph Network Reconnection label propagation algorithm Node Classification

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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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Complex network theory and game theory-based partitioning decision-making of parallel restoration for resilient power grid

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Wei, Xin Pan, Lianrong Xie, Daiyu Yang, Sheng Wei, Boyao Guangxi Power Grid Dispatching Control Ctr Nanning Peoples R China

The ability of fast restoration reflects power system resilience and safety. This paper proposes a partitioning decision-making method for parallel restoration based on the label propagation algorithm and the cooperative game theory. The topological and physical characteristics of blackout systems are considered as the edge weight and the node importance simultaneously for formulating the partitioning model. By the label propagation algorithm, various labels representing different subsystems mark all buses. To accelerate the speed of convergence and prevent the label oscillation, this paper proposes a game strategy of cooperation between buses and subsystems by evaluating the Shapley value of buses. The partitioning constraints are integrated into each label propagation process to assess the feasibility of the partitioning strategy. Finally, case studies on the IEEE 39- and 118-bus test systems and an actual urban power system in South China verify the effectiveness of the proposed method.

关键词： resilience power system restoration partitioning decision-making cooperative game theory label propagation algorithm

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Multiple black-start power supplies planning scheme considering partition recovery

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第PartB期215卷

作者： Zhao, Jie Liang, Yilin Fang, Yudi Weng, Yixuan Ma, Weizhe Zhang, Mingchen Li, Songhuan Wuhan Univ Sch Elect Engn & Automation Wuhan 430072 Peoples R China Shenzhen Power Co Ltd Shenzhen 518001 Peoples R China State Grid Fujian Elect Power Econ & Tech Res Inst Fuzhou 350000 Peoples R China

The number, layout and configuration of black-start power supplies will directly affect the recovery process of the power system after power outage. A scientific and reasonable black-start deployment optimization scheme can greatly accelerate the grid recovery process and reduce the duration of power outage. In this paper, based on the basic principles of power system partition recovery, we divide the power system recovery partition based on improved label propagation algorithm, which is used to guide the deployment optimization of multiple Blackstart power supplies sources. Subsequently, based on the important nodes and key lines of the grid, a mathematical model of multiple black-start power supplies deployment optimization considering the shortest restoration time is established, taking into account various constraints. Finally, the traditional coyote optimization algorithm is improved by improving the coyote growth model, introducing adaptive Le ' vy flight and chaotic optimization perturbation mechanism, and applied to the solution of multi-Black-start power supplies deployment optimization model. The effectiveness of the proposed partition recovery method and the adaptability and superiority of the multi-black-start power distribution optimization method are compared and analyzed by arithmetic examples in Matlab simulation software. The simulation results show that the proposed multi-black start power distribution optimization method with zonal recovery can reduce the outage duration and improve the recovery efficiency.

关键词： Black -start power supplies label propagation algorithm Partition recovery Coyote algorithm

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Analysis of pollutants transport in heavy air pollution processes using a new complex-network-based model

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ATMOSPHERIC ENVIRONMENT 2023年 292卷

作者： Chen, Mei Chen, Yongxu Zhu, Hongyu Wang, Youshuai Xie, Yue Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

A transport analysis model of air pollutants based on complex networks is proposed to study the spatiotemporal variation characteristics of air pollution during heavy air pollution processes and further analyze the contribution and interrelationship of air pollution of different cities. To construct the transmission network for each heavy air pollution process, a link is established between two cities according to the correlation coefficient of pollutants, the angle between the direction of cities and the wind direction, and its weight is assigned by integrating the factors of pollution, meteorology and geography. Then the PageRank algorithm is improved to identify the main contribution areas, and the label propagation algorithm is improved to find the associated air pollution areas. Finally, the period from January 1, 2014 to February 28, 2021 in Beijing-Tianjin-Hebei (BTH) region are used to demonstrate the performance of this model. The results show that a total of 58 heavy air pollution processes occur during the study period, the shortest lasting 3 days and the longest 13 days. These pollution processes occur mainly in winter, especially in January, and there are sufficient data to show that pollution levels decline year on year. Long-term correlation analysis for single-pollutant between different cities shows that geography is one of the most important factors affecting the regional transport of air pollutants. Moreover, according to the statistical analysis of each heavy pollution process, Tangshan, Tianjin and Langfang are the main contribution areas in the pollutant regional transport. In addition, apart from that in Zhangjiakou, Chengde and Qinhuangdao, the pollutants in the other cities in the BTH region have an effect on one another so that these cities are formed as an associated contaminated area. This study can provide better insights into local control and joint prevention of atmospheric pollution.

关键词： Transport analysis model Air pollution transmission network Heavy air pollution process PageRank algorithm label propagation algorithm Air pollutants

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2024年第5期23卷

作者： Gao, Liang Cui, Fang Zhang, Chengbo Suqian Univ Lib Suqian 223800 Peoples R China Inner Mongolia Univ Lib Hohhot 010021 Peoples R China

Science and technology are highly inheritable undertakings, and any scientific and technological worker can make good progress without the experience and achievements of predecessors or others. In the face of an ever-expanding pool of literature, the ability to efficiently and accurately search for similar works is a major challenge in current research. This paper uses Latent Dirichlet Allocation (LDA) topic model to construct feature vectors for the title and abstract, and the bag-of-words model to construct feature vectors for publication type. The similarity between the feature vectors is measured by calculating the cosine values. The experiment demonstrated that the precision, recall and WSS95 scores of the algorithm proposed in the study were 90.55%, 98.74% and 52.45% under the literature title element, and 91.78%, 99.58% and 62.47% under the literature abstract element, respectively. Under the literature publication type element, the precision, recall and WSS95 scores of the proposed algorithm were 90.77%, 98.05% and 40.14%, respectively. Under the combination of literature title, abstract and publication type elements, the WSS95 score of the proposed algorithm was 79.03%. In summary, the study proposes a robust performance of the literature screening (LS) algorithm based on the LDA topic model, and a similar LS system designed on this basis can effectively improve the efficiency of LS.

关键词： Support vector machine Naive Bayes network model latent dirichlet allocation topic model label propagation algorithm bag-of-words model

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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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A text-based mining approach for real estate policy impact monitoring and analysis

A text-based mining approach for real estate policy impact m...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Cao, Lei Xu, Peng Shang, Wei Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Vivo Mobile Commun Shenzhen Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781665439022

Text mining has been widely used in economic analysis. Besides extracting individual opinion from a single text, it is more important to accurately aggregate group opinions from mass text files. This research proposed a text mining framework for constructing policy impact indicators to monitoring the public opinions on real estate market policies. A real estate domain dictionary has been developed based on the released policy announcements in Chinese real estate government websites. And corresponding public opinion index are constructed to evaluate the impact of real estate policies on the target market. In addition, we have improved the handling of emotional words in the public opinion dictionary. Word2Vec and So-PMI models are used to identify the polarity of trend words, and the score of each sentiment words is calculated based on its co-occurrences with the trends words using a modified label propagation algorithm. Beijing real estate market is taken as the test bed. Empirical study show that the policy public opinion index explains a significant part of Beijing housing price changes. In-sample and out-of-sample forecasts of Beijing housing prices indicate that the policy public opinion index is capable of increase the prediction accuracy.

关键词： text mining policy public opinion index real estate market macroeconomic policy integrated learning label propagation algorithm

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Community Detection and Analysis in Air Pollution Complex Network Based on LPA 30

Community Detection and Analysis in Air Pollution Complex Ne...

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30th International Conference on Computer Communications and Networks (ICCCN)

作者： Lian, Jie Ding, Xin Pan, Jianguo Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 200234 Peoples R China

ISBN: (纸本)9780738113302

Air pollution has caused a bad effect on the natural environment and public health. With the rapid establishment of air monitoring stations and meteorological stations, the volume of air pollution data is increasing dramatically. The traditional methods to model the air pollution transmission process have shown a limitation to process a large amount of non-linear data. Machine learning methods have recently been proven to be extremely efficient and accurate to process data in complex structures. In this paper, a novel method based on a complex network is proposed to discover the air monitoring station communities and analyze the air pollutants diffusion. The method comprises several aspects, including a dimension reduction process, a complex network generation process, and a community detection process. The proposed method was verified based on a real-world air pollution dataset, and the results demonstrate that the air pollutants diffusion has a community structure, and some critical air monitoring stations have a greater influence on the transmission of pollutants, which should be paid more attention to the following air quality management.

关键词： air pollutants complex network community detection label propagation algorithm

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