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label propagation algorithm based on node similarity driven by local information

INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2019年第30期33卷

作者： Song, Chen Huang, Guoyan Yin, Bo Zhang, Bing Liu, Xinqian Yanshan Univ Coll Informat Sci & Engn 438 Hebei St Qinhuangdao 066004 Hebei Peoples R China 32153 Troops Chinese Peoples Liberat Army 56 Gebei St Zhangjiakou 075100 Hebei Peoples R China

label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.

关键词： Community detection complex network label propagation algorithm node similarity local information

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A spreading activation-based label propagation algorithm for overlapping community detection in dynamic social networks

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DATA & KNOWLEDGE ENGINEERING 2018年 113卷 155-170页

作者： Sattari, Mohammad Zamanifar, Kamran Univ Isfahan Dept Comp Engn Esfahan Iran

Community detection in temporal social networks is an increasingly challenging subject in network analysis. The label propagation algorithm (LPA) is a simple and fast approach for community detection in dynamic networks. However, it tends to generate monster communities which decrease the accuracy of community detection, especially in dynamic social networks. In this paper, we propose a modified LPA, called Spreading Activation label propagation algorithm in order to solve the problem. This method assigns a property, called activation value, to each label, where pairs (label name, activation value) are propagated by spreading activation process and the LPA. Furthermore, this algorithm uses two weighting algorithms, where each of them corresponds to one variation of the proposed method. Here, the variations of the proposed method and other available methods on real and synthetic networks are implemented. Experimental results on both real and synthetic networks show that all variations of the proposed method detect communities more accurately compared to the benchmark methods while they are slower than these methods.

关键词： Spreading activation label propagation algorithm Overlapping community detection Dynamic social networks

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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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A Degree-based Distributed label propagation algorithm for Community Detection in Networks 4

A Degree-based Distributed Label Propagation Algorithm for C...

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4th ACM SIGSPATIAL International Workshop on Safety and Resilience (EM-GIS)

作者： Dong, Jian Chen, Bin Ai, Chuan Liu, Liang Zhang, Fang Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781450360449

Community detection is an important way to understand the functions and characteristics of complex systems. The label propagation algorithm (LPA) is a community detection algorithm of which complexity is close to linear. Due to the randomness and instability of the algorithm, it is difficult to get good results with this algorithm. This paper proposes a degree-based label propagation algorithm and parallelizes the algorithm based on the GraphX component in Spark platform (D-disLPA). Using WeChat empirical network data, this paper adopts modularity as an evaluation index to compare D-disLPA with traditional label propagation algorithms. The experimental results show that the D-disLPA can effectively solve the non-convergence problem in traditional algorithms and improve the stability and accuracy of community detection. At the same time, this distributed algorithm is able to satisfy the requirements of large-scale community detection.

关键词： Community detection label propagation algorithm Pregel D-disLPA

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Site-Effect-Based Semi-Synchronous label propagation algorithm for Community Detection 16

Site-Effect-Based Semi-Synchronous Label Propagation Algorit...

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Liu, Tianqi Yan, Xuefeng Cen, Shenzhi Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728111414

The label propagation algorithm (LPA) for community detection has become prevalent due to its linear time complexity, and the number of communities does not need to be determined in advance when this algorithm is implemented. However, the results of LPA are unstable due to unavoidable randomness. A site-effect-based semi-synchronous label propagation algorithm (SE-SLPA) for community detection is proposed in this study. To deal with randomness, the algorithm divides nodes into correct communities quickly and accurately according to the three degrees of influence rule (TDIR). TDIR posits that different influence coefficients can be assigned to loops formed by nodes within three degrees, and the edge influence can be calculated to expedite label propagation and make the community structure visible. Results show that SE-SLPA improves modularity performance, normalized mutual information, stability, and iterative efficiency in community detection. This research is vital to the study of social networks.

关键词： Community detection label propagation algorithm Semi synchronous Site Effect Edge influence

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An improved label propagation algorithm based on node importance and random walk for community detection

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MODERN PHYSICS LETTERS B 2017年第14期31卷

作者： Ma, Tianren Xia, Zhengyou Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210015 Jiangsu Peoples R China

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits. label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability. Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure. \

关键词： Community detection label propagation algorithm random walk node importance

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Reinforcement label propagation algorithm Based on History Record 24th

Reinforcement Label Propagation Algorithm Based on History R...

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24th International Conference on Neural Information Processing (ICONIP)

作者： Liu, Kai Zhang, Yi Lu, Kai Wang, Xiaoping Wang, Xin Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China

ISBN: (纸本)9783319701394;9783319701387

With the continuous development of Internet, social networks are becoming more and more complex, and the research on these complex networks has attracted many researchers' attention. A large number of community discovery algorithms have emerged, among which the label propagation algorithm is widely used because of its simplicity and efficiency. However, this algorithm has poor stability due to the randomness in the label propagation process. To solve the problem, we propose a reinforcement label propagation algorithm (RLPA) in this paper. In RLPA, a similarity matrix is generated from the historical records of classification, which can be adopted to obtain the final result of community detection. The experimental results show that our algorithm can not only get better performance in accuracy, but also has higher stability.

关键词： Data mining Community discovery label propagation algorithm

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Eigenvector label propagation algorithm for Interactive Learning in Student Groups Based on Student Social Network 6

Eigenvector Label Propagation Algorithm for Interactive Lear...

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6th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Wang, Zhiping Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9781538604939

Interactive learning, which is based on data mining, is a hot issue and has attracted considerable attention recently. In this paper, we propose an Eigenvector label propagation algorithm (ELPA), which is improved from label propagation algorithm and solves three problems existing in original algorithm. The efficiency is improved greatly because of the introducing of 2-bit eigenvector label, which can reduce the size of exchanging data significantly. We compare the ELPA with GN and BMLPA on two famous benchmarks, and the experimental results show that the groups detected by ELPA are almost identical to the communities discovered by other LPA algorithms.

关键词： interactive learning community detection label propagation algorithm 2-bit eigenvector label

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Semantic community detection using label propagation algorithm

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JOURNAL OF INFORMATION SCIENCE 2016年第2期42卷 166-178页

作者： Kianian, Sahar Khayyambashi, Mohammad Reza Movahhedinia, Naser Univ Isfahan Fac Comp Engn Esfahan Iran

The issue of detecting large communities in online social networks is the subject of a wide range of studies in order to explore the network sub-structure. Most of the existing studies are concerned with network topology with no emphasis on active communities among the large online social networks and social portals, which are not based on network topology like forums. Here, new semantic community detection is proposed by focusing on user attributes instead of network topology. In the proposed approach, a network of user activities is established and weighted through semantic data. Furthermore, consistent extended label propagation algorithm is presented. Doing so, semantic representations of active communities are refined and labelled with user-generated tags that are available in web.2. The results show that the proposed semantic algorithm is able to significantly improve the modularity compared with three previously proposed algorithms.

关键词： Community detection label propagation algorithm semantic social network

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Signed Network label propagation algorithm with Structural Balance Degree for Community Detection 14th

Signed Network Label Propagation Algorithm with Structural B...

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14th International Conference on Smart Homes and Health Telematics (ICOST)

作者： Fang, Lei Yang, Qun Wang, Jiawen Lei, Weihua Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783319396019;9783319396002

Social networks are usually modeled as signed networks. The community detection is an important problem for the research of signed networks. The time complexity of signed label propagation algorithm is lower than most existing algorithms for community detection in the signed networks. However, bad performance on robustness and accuracy in the algorithm should not be ignored. Thus, we propose a structural balance degree to measure the balance of an edge in the local network and the local network density. Then a novel signed network label propagation algorithm with structural balance degree is proposed for community detection in signed networks. Besides, the algorithm is tested on several real-world social networks. Experimental results prove that the optimized algorithm can enhance both the robustness and the effectiveness. Its convergence rate is also faster than current algorithms.

关键词： Community detection Signed networks Structural balance degree label propagation algorithm

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