Label propagation algorithm based on node importance

Tao Wang, Shanshan Chen, Xiaoxia Wang and Jinfang Wang

Physica A: Statistical Mechanics and its Applications, 2020, vol. 551, issue C

Abstract: Label propagation algorithm (LPA) has attracted much attention due to its linear time complexity. However, there are disadvantages of uncertainty and randomness in the label propagation process, which may affect the stability and accuracy of community detection. In order to solve this problem, this paper proposes a novel label propagation algorithm based on node importance (NI-LPA). In the algorithm, a new index of node importance is presented which integrates the signal propagation of nodes, ks value of nodes themselves and Jaccard distance between adjacent nodes. The signal propagation considers the node importance from the perspective of network locality, the index reflects the position of nodes in the entire network, and Jaccard distance embodies the connection between nodes. The proposed index can fully reflect the node importance in the entire network. In the label propagation process, when the nodes with the maximum number of neighboring nodes are not unique, their labels are updated in terms of node importance. The proposed algorithm can avoid the instability caused by random selection in LPA algorithm. Experiments on real and synthetic networks show that NI-LPA can significantly improve the modularity of community and reduce the number of iterations. NI-LPA has better stability and accuracy than LPA.

Keywords: Label propagation; Community detection; Node importance; Complex networks (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:551:y:2020:i:c:s0378437120300042

DOI: 10.1016/j.physa.2020.124137

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