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

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.

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.

An improved label propagation algorithm based on node importance and random walk for community detection

2017 ◽  
Vol 31 (14) ◽  
pp. 1750162 ◽  
Author(s):  
Tianren Ma ◽  
Zhengyou Xia
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
High Efficiency ◽  
Linear Time ◽  
Structural Characteristics ◽  
Rapid Development ◽  
Time Algorithm ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm

Currently, with the rapid development of information technology, the electronic media for social communication is becoming more and more popular. Discovery of communities is a very effective way to understand the properties of complex networks. However, traditional community detection algorithms consider the structural characteristics of a social organization only, with more information about nodes and edges wasted. In the meanwhile, these algorithms do not consider each node on its merits.Label propagation algorithm (LPA) is a near linear time algorithm which aims to find the community in the network. It attracts many scholars owing to its high efficiency. In recent years, there are more improved algorithms that were put forward based on LPA. In this paper, an improved LPA based on random walk and node importance (NILPA) is proposed. Firstly, a list of node importance is obtained through calculation. The nodes in the network are sorted in descending order of importance. On the basis of random walk, a matrix is constructed to measure the similarity of nodes and it avoids the random choice in the LPA. Secondly, a new metric IAS (importance and similarity) is calculated by node importance and similarity matrix, which we can use to avoid the random selection in the original LPA and improve the algorithm stability.Finally, a test in real-world and synthetic networks is given. The result shows that this algorithm has better performance than existing methods in finding community structure.

A novel node gravitation-based label propagation algorithm for community detection

2019 ◽  
Vol 30 (06) ◽  
pp. 1950049 ◽  
Author(s):  
Mengjia Shen ◽  
Zhixin Ma
Keyword(s):  
Community Detection ◽  
Prior Information ◽  
Linear Time ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Node Importance ◽  
Propagation Algorithm ◽  
Community Detection Algorithm ◽  
And Function

Community detection in networks is a very important area of research for revealing the structure and function of networks. Label propagation algorithm (LPA) has been widely used to detect communities in networks because it has the advantages of linear time complexity and is unnecessary to get prior information, such as objective function and the number of communities. However, LPA has the shortcomings of uncertainty and randomness in the label propagation process, which affects the accuracy and stability of the algorithm. In this paper, we propose a novel community detection algorithm, named NGLPA, in which labels are propagated by node gravitation defined by node importance and similarity between nodes. To select the label according to the gravitation between nodes can reduce the randomness of LPA and is consistent with reality. The proposed method is tested on several synthetic and real-world networks with comparative algorithms. The results show that NGLPA can significantly improve the quality of community detection and obtain accurate community structure.

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.

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