Efficient Community Detection Based on Label Propagation with Belonging Coefficient and Edge Probability

2016 ◽  
pp. 54-72 ◽  
Author(s):  
Xinmeng Zhang ◽  
Yuefeng Li ◽  
Shengyi Jiang ◽  
Bailin Xie ◽  
Xia Li ◽  
...  
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Edge Probability

Community Detection by Motif-Aware Label Propagation

2020 ◽  
Vol 14 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Pei-Zhen Li ◽  
Ling Huang ◽  
Chang-Dong Wang ◽  
Jian-Huang Lai ◽  
Dong Huang
Keyword(s):  
Community Detection ◽  
Label Propagation

scholarly journals LPA-MNI: An Improved Label Propagation Algorithm Based on Modularity and Node Importance for Community Detection

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 497
Author(s):  
Huan Li ◽  
Ruisheng Zhang ◽  
Zhili Zhao ◽  
Xin Liu
Keyword(s):  
Community Detection ◽  
Real World ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Synthetic Networks

Community detection is of great significance in understanding the structure of the network. Label propagation algorithm (LPA) is a classical and effective method, but it has the problems of randomness and instability. An improved label propagation algorithm named LPA-MNI is proposed in this study by combining the modularity function and node importance with the original LPA. LPA-MNI first identify the initial communities according to the value of modularity. Subsequently, the label propagation is used to cluster the remaining nodes that have not been assigned to initial communities. Meanwhile, node importance is used to improve the node order of label updating and the mechanism of label selecting when multiple labels are contained by the maximum number of nodes. Extensive experiments are performed on twelve real-world networks and eight groups of synthetic networks, and the results show that LPA-MNI has better accuracy, higher modularity, and more reasonable community numbers when compared with other six algorithms. In addition, LPA-MNI is shown to be more robust than the traditional LPA algorithm.

Label propagation algorithm for community detection based on node importance and label influence

2017 ◽  
Vol 381 (33) ◽  
pp. 2691-2698 ◽  
Author(s):  
Xian-Kun Zhang ◽  
Jing Ren ◽  
Chen Song ◽  
Jia Jia ◽  
Qian Zhang
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm

An improved label propagation algorithm based on node intimacy for community detection in networks

2018 ◽  
Vol 32 (25) ◽  
pp. 1850279 ◽  
Author(s):  
Hanzhang Kong ◽  
Qinma Kang ◽  
Chao Liu ◽  
Wenquan Li ◽  
Hong He ◽  
...  
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Propagation Process ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Efficiency And Effectiveness ◽  
Specific Order ◽  
Topology Information ◽  
The Stability ◽  
Local Topology

Community detection in complex network analysis is a quite challenging problem spanning many applications in various disciplines such as biology, physics and social network. A large number of methods have been developed for this problem, among which the label propagation algorithm (LPA) has attracted much attention because of its advantages of nearly-linear running time and easy implementation. Nevertheless, the random updating order and tie-breaking strategy in LPA make the algorithm unstable and may even lead to the formation of a monster community. In this paper, an improved LPA called LPA-INTIM is proposed for solving the community detection problem. Firstly, an intimacy matrix is constructed using local topology information for measuring the intimacy between nodes. And then, the node importance is calculated to ensure that nodes are updated in a specific order. Finally, the label influence is evaluated for updating node label during the label propagation process. In addition, we introduce a novel tightness function to improve the stability of the proposed algorithm. By the comparison with the methods presented in the literatures, experimental results on real-world and synthetic networks show the efficiency and effectiveness of our proposed algorithm.

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