Circularly Searching Core Nodes Based Label Propagation Algorithm for Community Detection

With the application of community detection in complex networks becoming more and more extensive, the application of more and more algorithms for community detection are proposed and improved. Among these algorithms, the label propagation algorithm is simple, easy to perform and its time complexity is linear, but it has a strong randomness. Small communities in the label propagation process are easy to be swallowed. Therefore, this paper proposes a method to improve the partition results of label propagation algorithm based on the pre-partition by circularly searching core nodes and assigning label for nodes according to similarity of nodes. First, the degree of each node of the network is calculated. We go through the whole network to find the nodes with the maximal degrees in the neighbors as the core nodes. Next, we assign the core nodes’ labels to their neighbors according to the similarity between them, which can reduce the randomness of the label propagation algorithm. Then, we arrange the nodes whose labels had not been changed as the new network and find the new core nodes. After that, we update the labels of neighbor nodes according to the similarity between them again until the end of the iteration, to complete the pre-partition. The approach of circularly searching for core nodes increases the diversity of the network partition and prevents the smaller potential communities being swallowed in the process of partition. Then, we implement the label propagation algorithm on the whole network after the pre-partition. Finally, we adopt a modified method based on the degree of membership determined by the bidirectional attraction of nodes and their neighbor communities. This method can reduce the possibility of the error in partition of few nodes. Experiments on artificial and real networks show that the proposed algorithm can accurately divide the network and get higher degree of modularity compared with five existing algorithms.

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

International Journal of Modern Physics B ◽

10.1142/s021797921850279x ◽

2018 ◽

Vol 32 (25) ◽

pp. 1850279 ◽

Cited By ~ 2

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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Label propagation algorithm based on local cycles for community detection

International Journal of Modern Physics B ◽

10.1142/s0217979215500290 ◽

2015 ◽

Vol 29 (05) ◽

pp. 1550029 ◽

Cited By ~ 23

Author(s):

Xian-Kun Zhang ◽

Song Fei ◽

Chen Song ◽

Xue Tian ◽

Yang-Yue Ao

Keyword(s):

Community Structure ◽

Community Detection ◽

Real World ◽

Label Propagation ◽

Fast Method ◽

Propagation Process ◽

Node Label ◽

Propagation Algorithm ◽

Random Node ◽

Local Cycle

Label propagation algorithm (LPA) has been proven to be an extremely fast method for community detection in large complex networks. But an important issue of the algorithm has not yet been properly addressed that random update orders in label propagation process hamper the algorithm robustness of algorithm. We note that when there are multiple maximal labels among a node neighbors' labels, choosing a node' label from which there is a local cycle to the node instead of a random node' label can avoid the labels propagating among communities at random. In this paper, an improved LPA based on local cycles is given. We have evaluated the proposed algorithm on computer-generated networks with planted partition and some real-world networks whose community structure are already known. The result shows that the performance of the proposed approach is even significantly improved.

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Using Prior Knowledge for Community Detection by Label Propagation Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1049-1050.1566 ◽

2014 ◽

Vol 1049-1050 ◽

pp. 1566-1571

Author(s):

Yi Bo Wang ◽

Wen Jun Wang ◽

Dong Liu ◽

Xiao Liu ◽

Peng Fei Jiao

Keyword(s):

Community Detection ◽

Topological Structure ◽

Time Complexity ◽

Linear Time ◽

Label Propagation ◽

Detection Process ◽

Operation Speed ◽

Propagation Algorithm ◽

Improved Algorithm

Community detection is an important approach to analyze and understand the organization or unit structure of the complex networks. By comparing the existing community detection algorithms, the label propagation algorithm (LPA) shows prominent operation speed and qualifies near linear time complexity. However, original LPA algorithm only uses the topological structure to guide the community detection process, failing to improve the quality of community detection when extra information offered. In this paper, we combine the prior information with topological structure to guide the community detection process. During the label propagation process, we proposed a new label update principle, making a node absorb its neighbor label information depending on the label distribution. The experimental results both on real networks and artificial networks show that the improved algorithm not only inherits the characteristic of rapid speed, but also improves the quality of community detection. Moreover, the improved algorithm still has the feature of near linear time complexity.

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Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition

The Scientific World JOURNAL ◽

10.1155/2014/148686 ◽

2014 ◽

Vol 2014 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Aiping Zhang ◽

Guang Ren ◽

Yejin Lin ◽

Baozhu Jia ◽

Hui Cao ◽

...

Keyword(s):

Community Detection ◽

Computation Time ◽

Small Degree ◽

Label Propagation ◽

Small Community ◽

Percolation Transition ◽

Small Communities ◽

Numerical Tests ◽

Propagation Algorithm ◽

Update Process

Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss.

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

Entropy ◽

10.3390/e23050497 ◽

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.

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A Link-Based Similarity for Improving Community Detection Based on Label Propagation Algorithm

Journal of Systems Science and Complexity ◽

10.1007/s11424-018-7270-1 ◽

2018 ◽

Vol 32 (3) ◽

pp. 737-758 ◽

Cited By ~ 10

Author(s):

Kamal Berahmand ◽

Asgarali Bouyer

Keyword(s):

Community Detection ◽

Label Propagation ◽

Propagation Algorithm

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

Physics Letters A ◽

10.1016/j.physleta.2017.06.018 ◽

2017 ◽

Vol 381 (33) ◽

pp. 2691-2698 ◽

Cited By ~ 33

Author(s):

Xian-Kun Zhang ◽

Jing Ren ◽

Chen Song ◽

Jia Jia ◽

Qian Zhang

Keyword(s):

Community Detection ◽

Label Propagation ◽

Node Importance ◽

Propagation Algorithm

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

International Journal of Modern Physics C ◽

10.1142/s0129183118500110 ◽

2018 ◽

Vol 29 (02) ◽

pp. 1850011 ◽

Cited By ~ 5

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.

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