Detecting Multi-Scale Community Structure in Complex Networks

2015 ◽  
Vol 740 ◽  
pp. 881-884
Author(s):  
Yu Quan Guo ◽  
Xiong Fei Li
Keyword(s):  
Spectral Analysis ◽  
Community Structure ◽  
Complex Networks ◽  
Multiple Scale ◽  
Label Propagation ◽  
Dynamic Efficiency ◽  
Heuristic Function ◽  
Multi Scale ◽  
Propagation Algorithm ◽  
Novel Algorithm

Multiple-scale community of complex networks has attracted much attention. For the problem, previous methods can not investigate multiple-scale property of community. To address this, we propose a novel algorithm (h_LPA) to detect multiple-scale structure of community. The algorithm is a heuristic label propagation algorithm associated with spectral analysis of complex networks. Label updating strategy of h_LPA is combined with heuristic function from the perspective of networks dynamics. The heuristic function further improves the dynamic efficiency of h_LPA. Extensive tests on artificial networks and real world networks give excellent results.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

scholarly journals A Method To Improve The Time Of Computing For Detecting Community Structure In Social Network Graph

2019 ◽  
Vol 8 (6) ◽  
pp. 933-937
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Complex Networks ◽  
Large Scale ◽  
Linear Time ◽  
A Priori ◽  
Label Propagation ◽  
Data Sets ◽  
Network Graph ◽  
Priori Information

Identifying communities has always been a fundamental task in analysis of complex networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. Amongst them, the label propagation algorithm (LPA) brings great scaslability together with high accuracy but which is not accurate enough because of its randomness. In this paper, we study the equivalence properties of nodes on social network graphs according to the labeling criteria to shorten social network graphs and develop label propagation algorithms on shortened graphs to discover effective social networking communities without requiring optimization of the objective function as well as advanced information about communities. Test results on sample data sets show that the proposed algorithm execution time is significantly reduced compared to the published algorithms. The proposed algorithm takes an almost linear time and improves the overall quality of the identified community in complex networks with a clear community structure.

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 using average node energy in complex networks

2016 ◽  
Vol 460 ◽  
pp. 98-104 ◽  
Author(s):  
Hao Peng ◽  
Dandan Zhao ◽  
Lin Li ◽  
Jianfeng Lu ◽  
Jianmin Han ◽  
...  
Keyword(s):  
Complex Networks ◽  
Label Propagation ◽  
Propagation Algorithm ◽  
Node Energy
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