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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

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Differentially Private Clustered Federated Load Prediction Based on the louvain algorithm

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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

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

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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

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Self-adaptive louvain algorithm;Fast and stable community detection algorithm based on the principle of small probability event

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2018年 506卷 975-986页

作者： Zhang, Ziqiao Pu, Peng Han, Dingding Tang, Ming East China Normal Univ Sch Informat Sci & Technol Shanghai 200241 Peoples R China East China Normal Univ Sch Comp Sci & Software Engn Shanghai 200241 Peoples R China

Community structure is an important structure feature of complex networks. Due to its speed, effectiveness and simplicity, the louvain algorithm is widely used to detect community structure planted in the network topology. Speeding up the louvain algorithm, enabling the analysis of larger graphs in a shorter time and maintaining the accuracy of result, can benefit the research of networks in many fields. We here propose the Random Self-adaptive Neighbors louvain algorithm as a new improved louvain algorithm. The principle of small probability event is used to infer the number of neighbors to pick up randomly. The accuracy, speed and fluctuation of our method are compared with those of the original louvain algorithm and the Random Neighbor louvain algorithm. The results show that the RSNL can obtain as good partition as that of the original louvain in a faster speed. On the networks without distinct community structures, the RSNL is faster and more accurate than the RNL. A new measure, equivalent computing time, is proposed to show the expectation runtime of the algorithm to obtain a relatively good partition. The comparison of this measure shows that the RSNL algorithm can make the best performance among the three algorithms in most cases. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Community structure Modularity louvain algorithm Small probability event

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Large-Scale Network Representation Learning Based on Improved louvain algorithm and Deep Autoencoder 3rd

Large-Scale Network Representation Learning Based on Improve...

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3rd Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Xiong, Shou-Jiu Chen, Si-Bao Ding, Chris H. Q. Luo, Bin Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat MOE Key Lab ICSP Hefei 230601 Peoples R China Univ Texas Arlington Dept CSE Arlington TX 76019 USA

ISBN: (纸本)9783030606350;9783030606367

In recent years, feature learning of nodes in network has become a research hot spot. However, with the growth of the network scale, network structure has become more and more complicated, which makes it extremely difficult for network representation learning in large and complex networks. This paper proposes a fast large-scale network representation learning method based on improved louvain algorithm and deep autoencoder. First, it quickly folds large and complex network into corresponding small network kernel through effective improved louvain strategy. Then based on network kernel, a deep autoencoder method is conducted to represent nodes in kernel. Finally, the representations of the original network nodes are obtained by a coarse-to-refining procedure. Extensive experiments show that the proposed method perform well on large and complex real networks and its performance is better than most network representation learning methods.

关键词： Network representation learning Graph folding louvain algorithm Autoencoder Graph embedding

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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...

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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

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

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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

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Parallelizing louvain algorithm: Distributed Memory Challenges 16

Parallelizing Louvain Algorithm: Distributed Memory Challeng...

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16th IEEE Int Conf on Dependable, Autonom and Secure Comp/16th IEEE Int Conf on Pervas Intelligence and Comp/4th IEEE Int Conf on Big Data Intelligence and Comp/3rd IEEE Cyber Sci and Technol Congress (DASC/PiCom/DataCom/CyberSciTech)

作者： Sattar, Naw Safrin Arifuzzaman, Shaikh Univ New Orleans Dept Comp Sci New Orleans LA 70148 USA

ISBN: (纸本)9781538675182

louvain algorithm is a well-known and efficient method for detecting communities or clusters in social and information networks (graphs). The emergence of large network data necessitates parallelization of this algorithms for high performance computing platforms. There exist several shared-memory based parallel algorithms for louvain method. However, those algorithms do not scale to a large number of cores and large networks. Distributed memory systems are widely available nowadays, which offer a large number of processing nodes. However, the existing only MPI (message passing interface) based distributed-memory parallel implementation of louvain algorithm has shown scalability to only 16 processors. In this paper, we implement both shared-and distributed-memory based parallel algorithms and identify issues that hinder scalability. In our shared-memory based algorithm using OpenMP, we get 4-fold speedup for several real-world networks. However, this speedup is limited only by the physical cores available to our system. We then design a distributed-memory based parallel algorithms using message passing interface. Our results demonstrate an scalability to a moderate number of processors. We also provide an empirical analysis that shows how communication overhead poses the most crucial threat for deisgning scalable parallel louvain algorithm in a distributed-memory setting.

关键词： community detection louvain algorithm parallel algorithm distributed-memory MPI graph mining social networks

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Fuzzy Modularity Based louvain algorithm For Overlapping Community Detection 4

Fuzzy Modularity Based Louvain Algorithm For Overlapping Com...

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4th International Conference on Intelligent Information Processing (ICIIP)

作者： Cao, KeYin Liu, Yu Zhang, HuaXin Guilin Univ Technol Coll Informat Sci & Engn Guangxi Key Lab Embedded Technol & Intelligent Sy Guilin Peoples R China

ISBN: (纸本)9781450361910

Due to the fact that things may belong to multiple classifications, the traditional community partition algorithm cannot meet increasingly changing demands;the existing overlapping community partition algorithm still requires specific parameters in advance due to its poor stability. In this paper, a proposition for an overlapping community based on louvain algorithm is proposed. By introducing fuzzy modularity and calculating the membership matrix of the node for each community, the calculation of the membership matrix is optimized by using the local gradient descent algorithm, so that the node can be assigned to multiple communities simultaneously. With higher accuracy, solid stability and shorter running time (when compared with the original louvain algorithm), the algorithm in the larger community can be safely applied to the fields of recommendation or prediction.

关键词： louvain algorithm Fuzzy modularity Membership matrix Local gradient descent algorithm

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Enhancing Traffic Prediction with Interpretable Community Embeddings via louvain algorithm 12

Enhancing Traffic Prediction with Interpretable Community Em...

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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

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