Detecting an Overlapping Community Structure by Using Clique-to-Clique Similarity based Label Propagation

2019 ◽  
Vol 75 (6) ◽  
pp. 436-442 ◽  
Author(s):  
Hui Xie ◽  
Yongjie Yan
Keyword(s):  
Community Structure ◽  
Label Propagation ◽  
Overlapping Community

AGGLOMERATIVE CLUSTERING BASED ON LABEL PROPAGATION FOR DETECTING OVERLAPPING AND HIERARCHICAL COMMUNITIES IN COMPLEX NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450021 ◽  
Author(s):  
YUXIN ZHAO ◽  
SHENGHONG LI ◽  
SHILIN WANG
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Large Scale ◽  
Label Propagation ◽  
Agglomerative Clustering ◽  
Overlapping Communities ◽  
Small Communities ◽  
Structural And Functional Properties ◽  
Overlapping Community ◽  
Large Scale Networks

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm.

An algorithm Walktrap-SPM for detecting overlapping community structure

2017 ◽  
Vol 31 (15) ◽  
pp. 1750121 ◽  
Author(s):  
Fang Hu ◽  
Youze Zhu ◽  
Yuan Shi ◽  
Jianchao Cai ◽  
Luogeng Chen ◽  
...  
Keyword(s):  
Community Structure ◽  
Random Walk ◽  
Community Detection ◽  
Real World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Running Time ◽  
Overlapping Community ◽  
Negative Links ◽  
Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

scholarly journals The Overlapping Community Structure of Structural Brain Network in Young Healthy Individuals

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e19608 ◽  
Author(s):  
Kai Wu ◽  
Yasuyuki Taki ◽  
Kazunori Sato ◽  
Yuko Sassa ◽  
Kentaro Inoue ◽  
...  
Keyword(s):  
Community Structure ◽  
Brain Network ◽  
Healthy Individuals ◽  
Overlapping Community ◽  
Structural Brain Network ◽  
Structural Brain

scholarly journals Overlapping Community Detection Extended from Disjoint Community Structure

2019 ◽  
Vol 38 (5) ◽  
pp. 1091-1110
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Guibin Sun ◽  
Zhixiao Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Overlapping Community

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 Immunization of networks with non-overlapping community structure

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zakariya Ghalmane ◽  
Mohammed El Hassouni ◽  
Hocine Cherifi
Keyword(s):  
Community Structure ◽  
Overlapping Community
Sign in / Sign up

Export Citation Format

Share Document