检索结果-内蒙古大学图书馆

Differentially Private Clustered Federated Load Prediction Based on the louvain algorithm

algorithmS 2025年第1期18卷 32-32页

作者： Pan, Tingzhe Hou, Jue Jin, Xin Li, Chao Cai, Xinlei Zhou, Xiaodong CSG Sci Res Inst Co Ltd Guangzhou 510640 Peoples R China Guangdong Power Grid Co Ltd Power Dispatching Control Ctr Guangzhou 510060 Peoples R China

Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train personalized forecasting models without sharing load data. First, the similarity of user load patterns is calculated under privacy protection. Second, a complex network is constructed, and a federated user clustering method is developed based on the louvain algorithm, which divides users into multiple clusters based on load pattern similarity. Finally, a personalized and adaptive differentially private federated learning Long Short-Term Memory (LSTM) model for load forecasting is developed. A case study analysis shows that the proposed method can effectively protect user privacy and improve model prediction accuracy when dealing with heterogeneous data. The framework can train load-forecasting models with a fast convergence rate and better prediction performance than current mainstream federated learning algorithms.

关键词： federated learning load forecasting adaptive differential privacy louvain algorithm clustered

来源：评论

学校读者我要写书评

暂无评论

Swift Unfolding of Communities: GPU-Accelerated louvain algorithm 25

Swift Unfolding of Communities: GPU-Accelerated Louvain Algo...

引用

30th Symposium on Principles and Practice of Parallel Programming

作者： Wang, Zhibin Lin, Xi Li, Xue Wang, Pinhuan Meng, Ziheng Liu, Hang Tian, Chen Zhong, Sheng Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Alibaba Grp Hangzhou Peoples R China Rutgers State Univ New Brunswick NJ USA

ISBN: (纸本)9798400714436

The louvain algorithm is one of the most popular algorithms for community detection. Observing that existing implementations suffer from inaccurate pruning and inefficient intermediate state management, we introduce GALA, GPU-Accelerated louvain algorithm, which incorporates two key innovations. The first innovation is a novel modularity gainbased pruning strategy, supported by rigorous theoretical guarantees of optimality and able to reduce up to 76% of vertices as well as their corresponding computations. To take advantage of the memory hierarchy and parallelism of GPUs, the second innovation is workload-aware kernels, featuring a shuffle-based kernel founded on the warp-level primitives for exchange states and a hash-based kernel that prioritizes shared memory in hashtable design. GALA further scales to multiple GPUs by minimizing the synchronization overhead between GPUs through a dense-sparse synchronization strategy. We evaluate the performance of GALA through theoretical analysis and practical experiments on various real-world graphs. The experimental results confirm that GALA significantly improves the performance of the parallel louvain algorithm on GPUs, surpassing state-of-the-art solutions by 6x on average.

关键词： louvain algorithm community detection GPU

来源：评论

学校读者我要写书评

暂无评论

Leveraging the louvain algorithm for enhanced group formation and collaboration in online learning environments

引用

INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 2024年第1期21卷 1-28页

作者： Lee, Minkyung Sharma, Priya Penn State Univ Dept Learning & Performance Syst University Pk PA 16802 USA

This study explores the dynamics of student interaction networks within an online asynchronous discussion forum, focusing on both whole group discussions and subgroup interactions distinguished by the louvain algorithm, a renowned community detection method. Analyzing 2481 posts from 154 undergraduate students across three sections of a communications course centered on discussions about movie clips or social phenomena to enhance media literacy, this research aims to interpret the interaction patterns in these virtual spaces. Traditional methods of group formation, such as teacher intervention and self-selection, often fail to create balanced and effective groups, especially in large online courses. The louvain algorithm, known for its efficiency in modularity optimization, identifies clusters based on actual student interaction patterns. By leveraging both global and local network analyses, this study provides a comprehensive understanding of interaction structures. The global network analysis offers a macro view of overall interaction structures, revealing diverse patterns despite identical course designs, suggesting the influence of unique group dynamics. The local analysis, focusing on the intricacies of node and edge connections, underscores that the louvain algorithm's classifications correlate with heightened cohesiveness and collaborative potential. The results indicate that algorithmically detected groups exhibit strong internal communication and cohesiveness, as evidenced by high clustering coefficients, density values, and weighted degrees. These findings underscore the potential of network analysis to optimize online student interactions, providing valuable insights for refining educational design to promote student engagement and collaborative problem-solving. This research highlights the transformative potential of integrating advanced data-driven techniques in educational technology to improve group formation and collaborative learning outcomes, offer

关键词： louvain algorithm Group formation Asynchronous discussion Cohesiveness Network analysis Community detection Collaborative learning Educational technology

来源：评论

学校读者我要写书评

暂无评论

Minimum cost consensus model for CRP-driven preference optimization analysis in large-scale group decision making using louvain algorithm

引用

INFORMATION FUSION 2022年 80卷 121-136页

作者： Qin, Jindong Li, Minxuan Liang, Yingying Wuhan Univ Technol Sch Management Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Res Ctr Data Sci & Intelligent Decis Making Wuhan 430070 Hubei Peoples R China Beijing Inst Technol Sch Management & Econ Beijing 100081 Peoples R China

Large-scale group decision-making problems based on social network analysis and minimum cost consensus models (MCCMs) have recently attracted considerable attention. However, few studies have combined them to form a complete decision-making system. Accordingly, we define the satisfaction index to optimize the classical MCCM by considering the effect of the group on individuals. Similarly, we define the consistency index to optimize the consensus reaching process (CRP). Regarding the evolution of the consensus network, the louvain algorithm is used to divide the entire group into several subgroups to ensure that each subgroup is independent but has strong cohesion. By constructing the MCCM based on the satisfaction index and the optimized consensus-reaching process, the group opinions in each subgroup are ranked to obtain the final ranking of alternatives. Finally, to verify the validity of CRP and the practical value of the proposed model, we conduct consensus network evolution and decision-making analysis in the case of a negotiation between the government and polluting companies to achieve uniform pollution emissions. Sensitivity analysis is performed to demonstrate the stability of the subgroup weights. Furthermore, a comparative analysis using existing models verifies the effectiveness of the proposed model.

关键词： Large-scale group decision making Quadratic programming Minimum cost consensus model Consensus network evolution louvain algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhancing Traffic Prediction with Interpretable Community Embeddings via louvain algorithm 12

Enhancing Traffic Prediction with Interpretable Community Em...

引用

12th International Conference on Computer Technologies and Development (TechDev)

作者： Durys, Bartosz Pineda, Israel Lodz Univ Tech Inst Appl Comp Sci Lodz Poland Univ San Francisco Quito Dept Math Quito Ecuador

ISBN: (纸本)9798350381269;9798350381276

Predicting traffic is a complex problem that involves both space and time. This study focuses on the spatial aspect of this challenge, specifically how groups of road sections behave and interact within a city. Leveraging the well-regarded louvain algorithm, we partition the urban road network into distinct communities. To augment the predictive power of models, we implement a learnable embedding layer that integrates generated groups with the input. We test our idea with a classic and simple model called Temporal Graph Convolutional Network (T-GCN). The obtained results highlight the promise of this avenue of research and emphasize its value for further investigation. Notably, the interpretability of the generated embeddings is demonstrated. By extracting meaningful relationships and disparities among communities, we provide insights into the dynamics of the road network. This approach enhances traffic prediction and contributes to a deeper understanding of the spatial interactions within urban road systems.

关键词： community embeddings louvain algorithm interpretability traffic prediction

来源：评论

学校读者我要写书评

暂无评论

Enhancing Control Placement in SDN-IoT Networks Using the louvain algorithm and Betweenness-Centrality

引用

IEEE ACCESS 2024年 12卷 159775-159783页

作者： Abderrahmane, Amel Drid, Hamza Univ Batna 2 Dept Comp Sci LAMIE Lab Batna 05078 Algeria

The Internet of Things (IoT) continues to expand with an ever-growing number of devices. In this context, Software-Defined Networking (SDN) has emerged as a leading approach for effectively managing the dynamic and varied characteristics of IoT devices. Efficient controller placement is a crucial aspect of the SDN-IoT network, where the dynamic and diverse device connections demand responsive network management. This paper presents a novel approach for controller placement in SDN-IoT networks using the louvain algorithm combined with the Betweenness-Centrality method. The proposed method leverages the louvain algorithm to identify community structures within the network, representing groups of IoT devices with higher internal connectivity. Controllers are then strategically placed within these communities using the Betweenness-Centrality method. Our solution aims to minimize the number of controllers, reduce communication latency, and improve reliability. The modular nature of the louvain algorithm allows for scalable and adaptive network segmentation, accommodating changes in network topology and IoT device dynamics. We conduct comprehensive simulations to evaluate the performance of our approach, comparing it with an optimal controller placement strategy. The results indicate enhanced network responsiveness, decreased latency, and improved overall efficiency. Our solution seamlessly adapts to network fluctuations, ensuring resilience in dynamic SDN-IoT environments.

关键词： Internet of Things Heuristic algorithms Clustering algorithms Topology Software defined networking Scalability Partitioning algorithms Object recognition Delays Resource management Internet of Things (IoT) software-defined networking (SDN) SDN-IoT controller placement problem (CPP) louvain algorithm betweenness-centrality

来源：评论

学校读者我要写书评

暂无评论

An Anomaly Detection Method for Multiple Time Series Based on Similarity Measurement and louvain algorithm 3

An Anomaly Detection Method for Multiple Time Series Based o...

引用

3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Li, Shuya Song, Wenbin Zhao, Chao Zhang, Yifeng Shen, Weiming Hai, Jing Lu, Jiawei Xie, Yingshi Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Minist Environm Protect South China Inst Environm Sci Guangzhou 510655 Peoples R China Guangdong Prov Engn Lab Solid Waste Technol & Equ Guangzhou 510330 Peoples R China

Anomaly detection is an important task for data mining Detecting anomalies in the data collected from Municipal Solid Waste (MSW) incineration process is critical for their post processing, which helps to decrease emissions of typical flue gas pollutants and reduce costs. In this paper, we propose an unsupervised multiple time series anomaly detection model based on similarity measurement and louvain algorithm, which consists of two stages. The first stage aims to find a subset of correlated time series data. The second stage is to detect anomalies in the subset, which can identify anomalous time windows and anomalous time subseries in the anomaly windows. The proposed method is applied on the data collected from an MSW incinerator in South China The results demonstrate the effectiveness of the proposed method. In addition, four similarity measure approaches are conducted and the results show that the model performs the best when using Pearson correlation coefficient, with an F2-score of 0.92. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： municipal solid waste incineration anomaly detection machine-learning louvain algorithm similarity measurement

来源：评论

学校读者我要写书评

暂无评论

CBLA: A Clique Based louvain algorithm for Detecting Overlapping Community

引用

Procedia Computer Science 2023年 218卷 2201-2209页

作者： Sumit Kumar Gupta Dr. Dhirendra Pratap Singh

Graph mining is one of the significant tasks in the field of computer science. Most of the applications generate a vast amount of data which is represented with the help of a graph. Due to this graph representation, these applications have become complex and increased in size. Finding relevant information from that graph becomes a complicated task. For this community detection algorithms play a vital role in graph partitioning to retrieve relevant information. Finding communities in a graph reduces the complexity of the graph due to related data comes closer to forming a community. Many algorithms have been introduced in the last decade; the Clique percolation method (CPM) is the benchmark algorithm for finding an overlapping community. But in this method, some nodes remain unclassified, nodes that are not part of the clique. Paper proposed the clique-based louvain algorithm(CBLA), which can classify the non-classified node (NCN) obtained after finding cliques in one of the communities by applying the louvain algorithm. louvain algorithm is used to classify the non-overlapped community, but with the help of cliques, it will also detect the overlapped nodes. This paper compared the proposed algorithm with four other benchmark algorithms. The proposed algorithm gives equal or enhanced performance among all compared algorithms.

关键词： Community Detection Graph Partitioning Overlapping Community Detection CPM algorithm louvain algorithm

来源：评论

学校读者我要写书评

暂无评论

Expanding louvain algorithm for Clustering Relationship Formation

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 701-708页

作者： Murniyati Mutiara, Achmad Benny Wirawan, Setia Yusnitasari, Tristyanti Anggraini, Dyah Gunadarma Univ Comp Sci & Informat Technol Jakarta Indonesia

Community detection is a method to determine and to discover the existence of cluster or group that share the same interest, hobbies, purposes, projects, lifestyles, location or profession. There are some example of community detection algorithms that have been developed, such as strongly connected components algorithm, weakly connected components, label propagation, triangle count and average clustering coefficient, spectral optimization, Newman and louvain modularity algorithm. louvain method is the most efficient algorithm to detect communities in large scale network. Expansion of the louvain algorithm is carried out by forming a community based on connections between nodes (users) which are developed by adding weights to nodes to form clusters or referred to as clustering relationships. The next step is to perform weighting based on user relationships using a weighting algorithm that is formed by considering user account activity, such as giving each other recommendation comments, or to decide whether the relationship between the followers and the following is exist or not. The results of this study are the best modularity created with a value of 0.879 and the cluster test is 0.776.

关键词： Community detection louvain algorithm modularity network clustering relationship

来源：评论

学校读者我要写书评

暂无评论

An Anomaly Detection Method for Multiple Time Series Based on Similarity Measurement and louvain algorithm

引用

Procedia Computer Science 2022年 200卷 1857-1866页

作者： Shuya Li Wenbin Song Chao Zhao Yifeng Zhang Weiming Shen Jing Hai Jiawei Lu Yingshi Xie School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China South China Institute of Environmental Sciences Ministry of Environmental Protection Guangzhou 510655 China Guangdong Province Engineering Laboratory for Solid Waste Technology and Equipment Guangzhou 510330 China

Anomaly detection is an important task for data mining. Detecting anomalies in the data collected from Municipal Solid Waste (MSW) incineration process is critical for their post processing, which helps to decrease emissions of typical flue gas pollutants and reduce costs. In this paper, we propose an unsupervised multiple time series anomaly detection model based on similarity measurement and louvain algorithm, which consists of two stages. The first stage aims to find a subset of correlated time series data. The second stage is to detect anomalies in the subset, which can identify anomalous time windows and anomalous time subseries in the anomaly windows. The proposed method is applied on the data collected from an MSW incinerator in South China. The results demonstrate the effectiveness of the proposed method. In addition, four similarity measure approaches are conducted and the results show that the model performs the best when using Pearson correlation coefficient, with an F2-score of 0.92.

关键词： municipal solid waste incineration anomaly detection machine-learning louvain algorithm similarity measurement

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：