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第25届中国控制与决策会议

作者： Ai ping Zhang Guang Ren Hui Cao Bao zhu Jia Shu bin Zhang College of Marine Engineering Dalian Maritime University Department of Architectural Engineering Jilin Province Economic Management Cadre Colleg

ISBN: (纸本)9781467355339

We generalize the label propagation algorithm in complex networks to weighted networks by weighting the label propagation rule and the termination condition of label propagation algorithm. Experiments on computer-generated networks and real-world networks are carried out to compare the generalized algorithm with original algorithm. The results show the generalized algorithm is accurate, and the number of iteration is less. The weighed label propagation algorithm is applied to fault classification, and the good classification results are obtained.

关键词： weighted label propagation algorithm complex networks

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Detecting community structure using label propagation with consensus weight in complex network

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Chinese Physics B 2014年第9期23卷 594-601页

作者：梁宗文李建平杨帆 Athina Petropulu School of Computer Science and Engineering University of Electronic Science and Technology of China Electrical and Computer Engineering Department RutgersThe State University of New Jersey

Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight （LPAcw）, and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions.

关键词： label propagation algorithm community detection consensus cluster complex network

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