scholarly journals A Fast Overlapping Community Detection Algorithm with Self-Correcting Ability

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Laizhong Cui ◽  
Lei Qin ◽  
Nan Lu
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Community Structures ◽  
Overlapping Community Detection ◽  
Test Conditions ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
Evaluation Measurement ◽  
Repeat Visits ◽  
Membership Vector

Due to the defects of all kinds of modularity, this paper defines a weighted modularity based on the density and cohesion as the new evaluation measurement. Since the proportion of the overlapping nodes in network is very low, the number of the nodes’ repeat visits can be reduced by signing the vertices with the overlapping attributes. In this paper, we propose three test conditions for overlapping nodes and present a fast overlapping community detection algorithm with self-correcting ability, which is decomposed into two processes. Under the control of overlapping properties, the complexity of the algorithm tends to be approximate linear. And we also give a new understanding on membership vector. Moreover, we improve the bridgeness function which evaluates the extent of overlapping nodes. Finally, we conduct the experiments on three networks with well known community structures and the results verify the feasibility and effectiveness of our algorithm.

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.

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

2019 ◽  
Vol 33 (30) ◽  
pp. 1992001
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Line Graph ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Community Detection Algorithm
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