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.

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.

Label propagation algorithm based on node similarity driven by local information

2019 ◽  
Vol 33 (30) ◽  
pp. 1950363
Author(s):  
Chen Song ◽  
Guoyan Huang ◽  
Bo Yin ◽  
Bing Zhang ◽  
Xinqian Liu
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Local Information ◽  
Label Propagation ◽  
Selection Scheme ◽  
Original Algorithm ◽  
Large Scale Network ◽  
Propagation Algorithm ◽  
Node Similarity ◽  
Scale Network

Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.

scholarly journals A Node Influence Based Label Propagation Algorithm for Community Detection in Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Mu Zhu ◽  
Mengyu Shi ◽  
...  
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Detection Method ◽  
Label Propagation ◽  
Experimental Results ◽  
Superior Performance ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Poor Stability

Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods.

A label propagation approach based on local optimization

2018 ◽  
Vol 29 (06) ◽  
pp. 1850047
Author(s):  
Xiaohong Zhang ◽  
Yulin Jiang ◽  
Jianji Ren ◽  
Chaosheng Tang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Large Scale ◽  
Local Optimization ◽  
Label Propagation ◽  
Propagation Mechanism ◽  
Propagation Algorithm ◽  
Large Scale Networks ◽  
Special Factors

Community detection offers an important way to understand the structures and functions of social network. The label propagation algorithm has attracted vast attention since it is very suitable for discovering communities from large-scale networks. However, the algorithm suffers from the instability and inefficiency problem caused by the random policies it adopted. In this paper, we propose a novel label propagation approach based on local optimization to deal with the problem. The approach introduces a pre-propagation mechanism to optimize randomly initialized labels according to special factors, for example, node compactness. After that, it traverses and relabels nodes in the descending order of aggregate influence. The experiment results demonstrate the usefulness and effectiveness of our approach.

Using Prior Knowledge for Community Detection by Label Propagation Algorithm

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.

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.

scholarly journals Modularity-Based Incremental Label Propagation Algorithm for Community Detection

2020 ◽  
Vol 10 (12) ◽  
pp. 4060
Author(s):  
Yunlong Ma ◽  
Yukai Zhao ◽  
Jingwei Wang ◽  
Min Liu ◽  
Weiming Shen ◽  
...  
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Experimental Results ◽  
Optimal Partition ◽  
Propagation Algorithm ◽  
Optimization Function ◽  
Stable Performance ◽  
Community Detection Algorithm ◽  
Fine Tune

Label Propagation Algorithm (LPA) is a fast community detection algorithm. However, since each node is randomly assigned a different label at first, there is serious randomness in the label updating process of LPA, resulting in great instability of detection results. This paper proposes a modularity-based incremental LPA (MILPA) to address this problem. Unlike LPA, MILPA first assigns all nodes the same label, and then repeatedly uses divide strategy to split locally dense connected nodes into a community and give them a new label. After that, MILPA uses modularity gain as the optimization function to fine-tune the label of nodes so as to obtain an optimal partition. The proposed MILPA has been compared with LPA and other known methods. Experimental results show that MILPA has the best and most stable performance in LFR benchmark networks and is comparable to the best algorithm in many real networks.

Label Propagation Community Detection Algorithm Based on Density Peak Optimization

2021 ◽  
Author(s):  
Yan Ma ◽  
Guoqiang Chen
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Monitoring Network ◽  
Simple Algorithm ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Density Peak ◽  
Propagation Algorithm ◽  
Clustering Center ◽  
Community Detection Algorithm

Abstract Community structure detection in complex network structure and function to understand network relations, found its evolution rule, monitoring and forecasting its evolution behavior has important theoretical significance, in the epidemic monitoring, network public opinion analysis, recommendation, advertising push and combat terrorism and safeguard national security has wide application prospect. Label propagation algorithm is one of the popular algorithms for community detection in recent years, the community detection algorithm based on tags spread the biggest advantage is the simple algorithm logic, relative to the module of optimization algorithm convergence speed is very fast, the clustering process without any optimization function, and the initialization before do not need to specify the number of complex network community. However, the algorithm has some problems such as unstable partitioning results and strong randomness. To solve this problem, this paper proposes an unsupervised label propagation community detection algorithm based on density peak. The proposed algorithm first introduces the density peak to find the clustering center, first determines the prototype of the community, and then fixes the number of communities and the clustering center of the complex network, and then uses the label propagation algorithm to detect the community, which improves the accuracy and robustness of community discovery, reduces the number of iterations, and accelerates the formation of the community. Finally, experiments on synthetic network and real network data sets are carried out with the proposed algorithm, and the results show that the proposed method has better performance.

scholarly journals A Feasible Community Detection Algorithm for Multilayer Networks

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 223
Author(s):  
Dongming Chen ◽  
Panpan Du ◽  
Qianrong Jiang ◽  
Xinyu Huang ◽  
Dongqi Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Aggregation Model ◽  
Propagation Algorithm ◽  
Network Aggregation ◽  
Community Detection Algorithm

As a more complicated network model, multilayer networks provide a better perspective for describing the multiple interactions among social networks in real life. Different from conventional community detection algorithms, the algorithms for multilayer networks can identify the underlying structures that contain various intralayer and interlayer relationships, which is of significance and remains a challenge. In this paper, aiming at the instability of the label propagation algorithm (LPA), an improved label propagation algorithm based on the SH-index (SH-LPA) is proposed. By analyzing the characteristics and deficiencies of the H-index, the SH-index is presented as an index to evaluate the importance of nodes, and the stability of the SH-LPA algorithm is verified by a series of experiments. Afterward, considering the deficiency of the existing multilayer network aggregation model, we propose an improved multilayer network aggregation model that merges two networks into a weighted single-layer network. Finally, considering the influence of the SH-index and the weight of the edge of the weighted network, a community detection algorithm (MSH-LPA) suitable for multilayer networks is exhibited in terms of the SH-LPA algorithm, and the superiority of the mentioned algorithm is verified by experimental analysis.

Sign in / Sign up

Export Citation Format

Share Document