Density Peaks Based Clustering Algorithm for Overlapping Community Detection

2016 ◽  
Author(s):  
Hongtao Liu ◽  
Chaoyue Zhao ◽  
Yuan Tian ◽  
Juan Yang
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Overlapping Community Detection ◽  
Density Peaks ◽  
Overlapping Community

scholarly journals A link density clustering algorithm based on automatically selecting density peaks for overlapping community detection

2016 ◽  
Vol 30 (24) ◽  
pp. 1650167 ◽  
Author(s):  
Lan Huang ◽  
Guishen Wang ◽  
Yan Wang ◽  
Wei Pang ◽  
Qin Ma
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Overlapping Community Detection ◽  
Link Distance ◽  
Density Peaks ◽  
Link Density ◽  
Overlapping Community ◽  
Density Clustering ◽  
Relationship Of ◽  
The Relationship

In this paper, we proposed a link density clustering (LDC) method for overlapping community detection based on density peaks. We firstly use an extended cosine link distance metric to reflect the relationship of links. Then we introduce a clustering algorithm with fast search for solving the link clustering (LC) problem by density peaks with box plot strategy to determine the cluster centers automatically. Finally, we acquire both the link communities and the node communities. Our algorithm is compared with other representative algorithms through substantial experiments on real-world networks. The experimental results show that our algorithm consistently outperforms other algorithms in terms of modularity and coverage.

An overlapping community detection algorithm based on node distance of line graph

2019 ◽  
Vol 33 (26) ◽  
pp. 1950322 ◽  
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Line Graph ◽  
Detection Algorithm ◽  
Density Peak ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Normalized Mutual Information ◽  
Overlapping Community ◽  
Community Detection Algorithm

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Sign in / Sign up

Export Citation Format

Share Document