scholarly journals Overlapping Community Detection Method Based on Network Representation Learning and Density Peaks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226506-226514
Author(s):  
Hongtao Liu ◽  
Gege Li
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Representation Learning ◽  
Overlapping Community Detection ◽  
Network Representation ◽  
Density Peaks ◽  
Overlapping Community

scholarly journals A Local Overlapping Community Detection Method in Complex Networks

2012 ◽  
Vol 6-7 ◽  
pp. 985-990
Author(s):  
Yan Peng ◽  
Yan Min Li ◽  
Lan Huang ◽  
Long Ju Wu ◽  
Gui Shen Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Experimental Results ◽  
Overlapping Community Detection ◽  
Greedy Strategy ◽  
Structure Detection ◽  
Overlapping Community

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.

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.

scholarly journals Detecting Topic-oriented Overlapping Community Using Hybrid Hypergraph Model

2016 ◽  
Vol 11 (4) ◽  
pp. 538
Author(s):  
Gui-lan Shen ◽  
Xiao-ping Yang ◽  
Jie Sun
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Information Networks ◽  
Overlapping Community Detection ◽  
Structure Information ◽  
Hypergraph Model ◽  
Overlapping Community ◽  
New Challenges ◽  
Information Work ◽  
Real World Datasets

A large number of emerging information networks brings new challenges to the overlapping community detection. The meaningful community should be topicoriented. However, the topology-based methods only reflect the strength of connection, but ignore the consistency of the topics. This paper explores a topic-oriented overlapping community detection method for information work. The method utilizes a hybrid hypergraph model to combine the node content and structure information naturally. Two connections for hyperedge pair, including real connection and virtual connection are defined. A novel hyperedge pair similarity measure is proposed by combining linearly extended common neighbors metric for real connection and incremental fitness for virtual connection. Extensive experiments on two real-world datasets validate our proposed method outperforms other baseline algorithms.

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