Abstract

Label propagation algorithm is one of the popular community detection algorithms in recent years. The advantages of the community detection algorithm based on label propagation are that the algorithm logic is simple, compared with the modularity optimization algorithm, the convergence speed is very fast, the whole clustering process does not need any optimization function, and there is no need to specify the number of communities in the complex network before initialization. However, this algorithm has problems such as unstable partitioning results and strong randomness. In order to solve these problems, this paper proposes a semi-supervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to discover the cluster centers, determines the prototype of community, fixes the number of communities and cluster centers in complex network, and then uses label propagation algorithm to detect communities, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the communities.

Highlights

  • Community structure is a very important attribute in complex networks

  • This paper proposes an unsupervised label propagation community detection algorithm based on density peak

  • We are inspired by the density peak algorithm (DP) [8] and propose a label propagation algorithm based on density peak (DPLPA) for solving complex networks

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Summary

INTRODUCTION

Community structure is a very important attribute in complex networks. community structure plays a crucial role in the analysis of the social relations in human society [1], and in the analysis of the functional relations between biological network organizations and organs [2], as well as the analysis of the citation relations between collaborative networks among scientists [3]. The basic idea of label propagation is to predict the label information of unlabeled nodes by using the topological relations between nodes from the label information of labeled nodes, and complete the division of the graph to form a clustering structure. This algorithm has the advantages of simple implementation, clear logic, no need to know the number of communities in advance, time complexity is close to linearity, etc., the unstable partition results and strong randomness are the defects of this algorithm. DP algorithms cannot be directly used in complex network, so we to DP algorithm is improved, and it can be applied to a complex network, can be reasonably come to the core number, applied to the label propagation algorithm, according to the topology of the network that similarity matrix and priority to update nodes, reduce the randomness and the number of iterations

Label Propagation Algorithm
Density Peak Algorithm
METHODOLOGY
Predictive Fetch of Label Matrix
Label Propagation Algorithm Based on Density Peak
EXPERIMENTAL STUDY
Evaluation Metrics
Performance on Synthetic Networks
Real-World Networks
Findings
CONCLUDING REMARKS
Full Text
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