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label propagation algorithm based on edge clustering coefficient for community detection in complex networks

INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2014年第30期28卷 1450216-1450216页

作者： Zhang, Xian-Kun Tian, Xue Li, Ya-Nan Song, Chen Tianjin Univ Sci & Technol Coll Comp Sci & Informat Technol Tianjin 300222 Peoples R China

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm.

关键词： Complex network community discovery label propagation algorithm edge clustering coefficient

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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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Memory-based label propagation algorithm for community detection in social networks

Memory-based label propagation algorithm for community detec...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Hosseini, Razieh Azmi, Reza Alzahra Univ Dept Comp Engn OSSL Tehran Iran

ISBN: (纸本)9781479988181

Community detection in social network is a significant issue in the study of the structure of a network and understanding its characteristics. A community is a significant structure formed by nodes with more connections between them. In recent years, several algorithms have been presented for community detection in social networks among them label propagation algorithm is one of the fastest algorithms, but due to the randomness of the algorithm its performance is not suitable. In this paper, we propose an improved label propagation algorithm called memory-based label propagation algorithm (MLPA) for finding community structure in social networks. In the proposed algorithm, a simple memory element is designed for each node of graph and this element store the most frequent common adoption of labels iteratively. Our experiments on the standard social network datasets show a relative improvement in comparison with other community detection algorithms.

关键词： label propagation algorithm community detection social networks complex networks

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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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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 for Undirected Weighted Networks 12

An Improved Label Propagation Algorithm for Undirected Weigh...

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12th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC)

作者： Li, Peilei Yu, Hanging Zhang, Yijin Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing Peoples R China

ISBN: (纸本)9798350374377

In recent years, with the rapid development of network technology, the scale of social networks has become increasingly large and diversified. The detection of community has become a hot spot in the research of social network. label propagation algorithm (LPA) has been widely concerned since it has the advantages of linear time complexity. However, the randomness of LPA affects the accuracy and stability of the community. This paper proposes an improved label propagation algorithm for undirected weighted networks. In the first stage, considering both node importance and local topological information, an improved LPA is designed to divide the undirected weighted network into initial communities, thereby reducing the instability of LPA. In the second stage, a strategy for increasing modularity is designed to merge small communities, thereby improving the quality of community partitioning. Experimental results show that the proposed community detection algorithm has better community detection quality compared to LPA and four classical community detection algorithms.

关键词： Community detection LeaderRank algorithm 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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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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Academic Tweet Classification with Spreading activation based label propagation algorithm using Tweet centric features 16

Academic Tweet Classification with Spreading activation base...

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1st International Conference on Informatics and Analytics (ICIA)

作者： Manju, G. Geetha, T., V Coll Engn Guindy Chennai Tamil Nadu India

ISBN: (纸本)9781450347563

Social network like Twitter is used by researchers and academicians to develop their professional relationship and as well it acts as a communication tool to share their research ideas, and research results. Among the enormous number of tweets, certain tweets contain general information and others might contain academic related information that aid scholarly communication. In order to identify such tweets, our work aims to classify the tweets as academic or non-academic tweet based on new combination of tweet content features like hashtags, terms that influence academic domain and user related features like follower/followee graph, mentions and tweet reply status. Spreading activation based label propagation algorithm is used for tweet classification. In order to choose the appropriate random neighbor and effectively propagate the labels, the activation value computed by Spreading activation is used. Further, this helps to reduce the number of iterations and thereby, speeds up the convergence of the label propagation algorithm. The comprehensive experimental results shows that spreading activation based label propagation algorithm effectively classifies the tweets as academic or non-academic using the inferred academic feature combination with marginal decrease in the number of iterations.

关键词： Tweet Classification label propagation algorithm Academic Tweet Twitter Spreading activation algorithm

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A low complexity nonlinearity impairment compensation scheme assisted by label propagation algorithm

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OPTICS COMMUNICATIONS 2022年 506卷 127547-127547页

作者： Li, Baokun Bai, Chenglin Xu, Hengying Yang, Lishan Sun, Weibin Yu, Xinkuo Zhao, Ruqing Zhou, Tanglei Ge, Peiyun Lv, Xiuhua Luo, Xueyuan Liaocheng Univ Sch Phys Sci & Informat Engn Liaocheng 252000 Shandong Peoples R China Shandong Prov Key Lab Opt Commun Sci & Technol Liaocheng 252000 Shandong Peoples R China

In this paper, we firstly applied label propagation algorithm (LPA) to coherent optical communication system for nonlinear impairment compensation (NLC), which could exploit the relationship among constellation points to segment the community with only very small amount of label data to predict the label situation on most test data. The effectiveness of the proposed scheme was verified through 28 GBaud polarization division multiplexing (PDM)-16 quadrature amplitude modulation (QAM), PDM-32QAM and PDM-64QAM simulation system. By analyzing the simulation results, we obtained the best propagation coefficient alpha, which demonstrated that the proposed scheme could effectively make nonlinear decision on received symbols and compensate signal impairment caused by Kerr nonlinear effect and amplified spontaneous emission (ASE) noise, showing a strong robustness and generalization to variations of the transmission environment under different modulation formats, transmission distances, launch power and channel numbers. Then, the proposed scheme was further studied by 20 GBaud PDM-16QAM long-haul transmission experiments. The results demonstrated that the proposed scheme required no training process and exhibited optimistic compensation performance to the coherent optical communication system with nonlinear impairments. Finally, we analyzed computational complexity of the LPA-assisted NLC scheme in detail, which was O(m*N).

关键词： Coherent optical communication Nonlinear impairment compensation label propagation algorithm Single channel Wavelength division multiplexing Polarization division multiplexing

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