Motif‐based embedding label propagation algorithm for community detection

2021 ◽  
Author(s):  
Chunying Li ◽  
Yong Tang ◽  
Zhikang Tang ◽  
Jinli Cao ◽  
Yanchun Zhang
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Propagation Algorithm

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.

LPA-CBD an improved label propagation algorithm based on community belonging degree for community detection

2018 ◽  
Vol 29 (02) ◽  
pp. 1850011 ◽  
Author(s):  
Chun Gui ◽  
Ruisheng Zhang ◽  
Zhili Zhao ◽  
Jiaxuan Wei ◽  
Rongjing Hu
Keyword(s):  
Community Detection ◽  
Maximum Degree ◽  
Label Propagation ◽  
General Process ◽  
Network Community ◽  
Propagation Algorithm ◽  
Community Belonging ◽  
Original Label ◽  
The Stability ◽  
Detection Quality

In order to deal with stochasticity in center node selection and instability in community detection of label propagation algorithm, this paper proposes an improved label propagation algorithm named label propagation algorithm based on community belonging degree (LPA-CBD) that employs community belonging degree to determine the number and the center of community. The general process of LPA-CBD is that the initial community is identified by the nodes with the maximum degree, and then it is optimized or expanded by community belonging degree. After getting the rough structure of network community, the remaining nodes are labeled by using label propagation algorithm. The experimental results on 10 real-world networks and three synthetic networks show that LPA-CBD achieves reasonable community number, better algorithm accuracy and higher modularity compared with other four prominent algorithms. Moreover, the proposed algorithm not only has lower algorithm complexity and higher community detection quality, but also improves the stability of the original label propagation algorithm.

LILPA: A label importance based label propagation algorithm for community detection with application to core drug discovery

2020 ◽  
Vol 413 ◽  
pp. 107-133 ◽  
Author(s):  
Yun Zhang ◽  
Yongguo Liu ◽  
Qiaoqin Li ◽  
Rongjiang Jin ◽  
Chuanbiao Wen
Keyword(s):  
Drug Discovery ◽  
Community Detection ◽  
Label Propagation ◽  
Propagation Algorithm

scholarly journals Local memory boosts label propagation for community detection

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Antonio Maria Fiscarelli ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Linear Time ◽  
Topological Analysis ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Performance Study ◽  
Global Knowledge ◽  
Local Optima ◽  
Propagation Algorithm

Abstract The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high complexity and require global knowledge of the network, which makes them less suitable for large-scale networks. The Label Propagation Algorithm initially assigns a distinct label to each node that iteratively updates its label with the one of the majority of its neighbors, until consensus is reached among all nodes in the network. Nodes sharing the same label are then grouped into communities. It runs in near linear time and is decentralized, but it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a variation of the classical Label Propagation Algorithm where each node implements a memory mechanism that allows them to “remember” about past states of the network and uses a decision rule that takes this information into account. We demonstrate through extensive experiments, on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks, that MemLPA outperforms other existing label propagation algorithms that implement memory and some of the well-known community detection algorithms. We also perform a topological analysis to extend the performance study and compare the topological properties of the communities found to the ground-truth community structure.

Semantic community detection using label propagation algorithm

2015 ◽  
Vol 42 (2) ◽  
pp. 166-178 ◽  
Author(s):  
Sahar Kianian ◽  
Mohammad Reza Khayyambashi ◽  
Naser Movahhedinia
Keyword(s):  
Community Detection ◽  
Label Propagation ◽  
Propagation Algorithm
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