EADP: An extended adaptive density peaks clustering for overlapping community detection in social networks

2019 ◽  
Vol 337 ◽  
pp. 287-302 ◽  
Author(s):  
Mingli Xu ◽  
Yuhua Li ◽  
Ruixuan Li ◽  
Fuhao Zou ◽  
Xiwu Gu
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Overlapping Community

Community Detection in Large-Scale Social Networks

2018 ◽  
pp. 189-206 ◽  
Author(s):  
S Rao Chintalapudi ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
The Other ◽  
Edge Density ◽  
Challenging Problem ◽  
Overlapping Community Detection ◽  
Clustering Problem ◽  
Detection Algorithms ◽  
Overlapping Community

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

Overlapping community detection in heterogeneous social networks via the user model

2018 ◽  
Vol 432 ◽  
pp. 164-184 ◽  
Author(s):  
Mingqing Huang ◽  
Guobing Zou ◽  
Bofeng Zhang ◽  
Yue Liu ◽  
Yajun Gu ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
User Model ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Heterogeneous Social Networks
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