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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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Detecting Dynamic and Static Geo-Social Communities

Detecting Dynamic and Static Geo-Social Communities

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4th International Conference on Computing for Geospatial Research and Application (COM.Geo)

作者： Lin, Frank Renner, Ray Northrop Grumman Informat Syst Mclean VA 22102 USA Northrop Grumman Informat Syst Chantilly VA USA

ISBN: (纸本)9780769550121

How communities form can depend on the geospatial location of people within a social network. Here, we investigated the implementation of the label propagation algorithm (LPA) and labelRankT community detection algorithm in Gephi, a graph visualization tool. We researched extending these community detection algorithms to incorporate the geospatial distance between nodes in a network as a limiting factor for the automatic detection of community formation.

关键词： Graph algorithms Social Network Analysis Geo-Social Community Detection label propagation algorithm

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Detecting Network Community by Propagating labels Based on Contact-Specific Constraint 11th

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11th International Scientific Conference on Future Information Technology (FutureTech) / 10th International Conference on Multimedia and Ubiquitous Engineering (MUE)

作者： Wu, Xiaolan Zhang, Chengzhi Nanjing Univ Sci & Technol Dept Informat Management Nanjing 210094 Jiangsu Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Anhui Peoples R China Key Lab Data Serv & Knowledge Engn Nanjing 210093 Jiangsu Peoples R China

ISBN: (纸本)9789811015366;9789811015359

As a famous community detection algorithm with near linear time complexity, label propagation algorithm (LPA) has been an active research direction. However, that LPA regards that all the neighbor have the same importance to target node in target node's label update process make it get stuck in poor stability. From the view of structural holes in sociology, people who fill structural holes bring social order to the network, so we improve LPA based on contact-specific constraint measuring structural holes. Experiments show that our improvement successfully detects communities with the highest stability in several commonly used real-world and synthetic networks.

关键词： label propagation algorithm Community detection Network constraint Structural holes Social network

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CLAN: An Efficient Distributed Temporal Community Detection Protocol for MANETs 11

CLAN: An Efficient Distributed Temporal Community Detection ...

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11th IEEE International Conference on Mobile Ad Hoc and Sensor Systems

作者： Dabideen, Stephen Kawadia, Vikas Nelson, Samuel C. Raytheon BBN Technol Cambridge MA 02138 USA

ISBN: (纸本)9781479960361

Real world MANETs often exhibit an inherent community structure in their topological connectivity and in the evolution of the topology over time. Such temporal community structure of MANETs has been shown to be extremely useful in improving the performance of routing and content-based routing in MANETs [1]-[4]. However, detecting temporal communities in a completely distributed and real time manner is a hard problem, and it is often performed offline with knowledge of the full network topology over time. We propose CLAN, a distributed and real-time protocol for detecting temporal communities in MANETs. CLAN is an adaptation of the label propagation algorithm [5] to distributed and time-varying graphs that MANETs are. A key novel component of CLAN is local rules for community rediscovery as the network evolves. CLAN also uses a weighted version of the network topology where the weights are defined using a novel notion of social entropy to promote stability of communities. Extensive simulation results demonstrate that CLAN is quick to converge, incurs minimal overhead and is as effective as centralized approaches to temporal community detection. We also demonstrate how the temporal community structure can be used by designing a hierarchical routing protocol that achieves the delivery ratio of the OLSR [6] routing protocol at a fraction of the overhead.

关键词： graph theory mobile ad hoc networks routing protocols telecommunication network topology CLAN protocol OLSR routing protocol community rediscovery content-based routing distributed graph distributed-real-time protocol efficient distributed temporal community detection protocol hard problem hierarchical routing protocol inherent community structure label propagation algorithm network topology real-world MANET social entropy temporal community structure time-varying graph topological connectivity Ad hoc networks Communities Entropy Mobile computing Network topology Protocols Routing Community detection label propagation ad-hoc clustering Mobile ad hoc networks Network topology Hierarchical routing protocol Community structure telecommunication network topology Routing protocols Network routing protocols Mobile Computing Ad hoc networks Graph theory entropy

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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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Using big data searching and machine learning to predict human health risk probability from pesticide site soils in China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022年 320卷 115798页

作者： Wang, Xin Yu, Dongsheng Ma, Lixia Lu, Xiaosong Song, Jie Lei, Mei Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 210008 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Soil pollutants emitted from pesticide sites seriously threaten human health, and predicting the human health risk (HHR) is necessary to the control and management of these sites. A database of 1933 pesticide sites in China, including 32 surveyed site samples, was created through big data searching and cleaning. First, six risk predic-tion indicators were screened through a correlation analysis, ANOVA, and cardinality test. The six indicators included soil pollution proba-bility, proportions of the population over 60 and under 15 years old, soil sand at the 30-100 cm layer, elevation, and proportion of arable land area within 1 km of the sites. Second, the surveyed samples were divided into training and testing sets at a ratio of 8:2, and the synthetic minority oversampling technique was used to achieve sample size rebalancing in the training set between positive and counter examples. Two semi -supervised learning models based on the self-training method and label propagation algorithm (SSL-LPA) were trained and tested. SSL-LPA was screened for the prediction of HHR probability, and it had the highest prediction accuracy with a cross-validation accuracy of 93% and recall rate of 100%. Lastly, about 3.7% of the un-surveyed sites were predicted by SSL-LPA as risky sites with an HHR probability of over 0.94, and they were mainly distributed in northern and eastern China. These sites were predicted as risky due to the effects of soil pollution proba-bility and the spatial distribution of pesticide sites, especially in the surveyed risky sites. This study provides technical support for HHR control and management of pesticide sites in China.

关键词： Pesticide site Human health risk Risk probability prediction Semi -supervised learning model 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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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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Graph-Based Semi-Supervised Learning for Activity labeling in Health Smart Home

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IEEE ACCESS 2020年 8卷 193655-193664页

作者： Hu, Yan Wang, Bingce Sun, Yuyan An, Jing Wang, Zhiliang Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Beijing Engn & Technol Ctr Convergence Networks & Beijing 100083 Peoples R China Chinese Acad Sci Beijing Key Lab IoT Informat Secur Inst Informat Engn Beijing 100195 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100195 Peoples R China Univ Sci & Technol Beijing Sch Marxism Studies Beijing 100083 Peoples R China

Health Smart Home (HSH) is an important part of smart city. This technology provides a new kind of remote medical treatment, and can effectively alleviate the shortage of medical resources caused by aging population and help elderly people live at home more safely and independently. Activity recognition is the core of Health Smart Home. However, constructing activity recognition models usually requires a large amount of labeled data, which imposes a heavy burden on manual labeling. In this article, the authors propose an activity labeling approach based on a graph-based semi-supervised learning algorithm. This approach can divide the raw sensor event sequence without any label information into appropriate segments. Consecutive sensor events that occurred in a same activity are grouped into a same segment. In addition, this approach requires only a small number of manually labeled segments to complete the labeling of the remaining large number of unlabeled segments, thereby greatly reducing the burden of manual labeling. After that, all the labeled data can be further used for activity recognition in smart homes. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed activity labeling approach.

关键词： Monitoring labeling Smart homes Activity recognition Training Senior citizens Temperature sensors Smart city health smart home activity labeling semi-supervised learning label propagation algorithm

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Research Notes: User Influence in Microblog Based on Interest Graph

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International Journal of Software Engineering and Knowledge Engineering 2018年第2期28卷 207-217页

作者： Lidong Zhai Jingya Wang Chaojian Hu Jun Li Institute of Computing Technology Chinese Academy of Sciences Haidian District Beijing 100190 P. R. China Tencent Xuanwu Lab Haidian District Beijing 100193 P. R. China

Microblog is currently the largest social networking platform in China. In recent years, as a social media, the influence of microblog continues to expand. The users who have large influence play a guiding role in the spread of microblog, and even guide the trends of public opinion. Therefore, we propose an influence analysis method to find microblog users who are with great influence, which is of great significance for the research and mining of microblog. User influence analysis in microblog has great difficulties due to the limited amount of microblog information, quick updates and nonstandard microblog language. First, we use the label propagation algorithm combined with LDA algorithm to divide users by the user interest graph, according to the social relationship of microblog users and the content they generate. Then, depending on different interest areas, an improved PageRank algorithm based on user interaction behavior is proposed to calculate the user’s influence. Experiments on the real datasets show that the proposed method outperforms the traditional algorithms.

关键词： Microblogs LDA label propagation algorithm interest graph influence calculation

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