Network representation learning based on community-aware and adaptive random walk for overlapping community detection

2022 ◽  
Author(s):  
Kun Guo ◽  
Qinze Wang ◽  
Jiaqi Lin ◽  
Ling Wu ◽  
Wenzhong Guo ◽  
...  
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
Representation Learning ◽  
Overlapping Community Detection ◽  
Network Representation ◽  
Overlapping Community

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 Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222956-222965
Author(s):  
Dong Liu ◽  
Qinpeng Li ◽  
Yan Ru ◽  
Jun Zhang
Keyword(s):  
Random Walk ◽  
Learning Algorithm ◽  
Representation Learning ◽  
Network Representation

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.

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