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.

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

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.

Label propagation algorithm based on local cycles for community detection

2015 ◽  
Vol 29 (05) ◽  
pp. 1550029 ◽  
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.

Label propagation algorithm based on edge clustering coefficient for community detection in complex networks

2014 ◽  
Vol 28 (30) ◽  
pp. 1450216 ◽  
Author(s):  
Xian-Kun Zhang ◽  
Xue Tian ◽  
Ya-Nan Li ◽  
Chen Song
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Learning Algorithm ◽  
Low Complexity ◽  
Label Propagation ◽  
Clustering Coefficient ◽  
Propagation Algorithm ◽  
The Stability ◽  
Stability And Accuracy ◽  
Edge Clustering Coefficient

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the 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.

Circularly Searching Core Nodes Based Label Propagation Algorithm for Community Detection

2016 ◽  
Vol 30 (08) ◽  
pp. 1659024 ◽  
Author(s):  
Ronghua Shang ◽  
Weitong Zhang ◽  
Licheng Jiao
Keyword(s):  
Community Detection ◽  
Time Complexity ◽  
Label Propagation ◽  
Propagation Process ◽  
Small Communities ◽  
Network Partition ◽  
The Core ◽  
Modified Method ◽  
Propagation Algorithm ◽  
Degree Of Membership

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.

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.

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