Overlapping community detection on complex networks with Graph Convolutional Networks
S Yuan, H Zeng, Z Zuo, C Wang - Computer Communications, 2023 - Elsevier
Discovering the community structure within networks is of significance with respect to many
realistic applications, like recommendation systems and cyberattack detection. In this study …
realistic applications, like recommendation systems and cyberattack detection. In this study …
Community detection with graph neural network using Markov stability
S Yuan, C Wang, Q Jiang, J Ma - … International Conference on …, 2022 - ieeexplore.ieee.org
Community detection is a fundamental task in network analysis. With the recent
development of deep learning, some community detection methods related to deep learning …
development of deep learning, some community detection methods related to deep learning …
Network structural transformation-based community detection with autoencoder
X Geng, H Lu, J Sun - Symmetry, 2020 - mdpi.com
In this paper, we proposed a novel community detection method based on the network
structure transformation, that utilized deep learning. The probability transfer matrix of the …
structure transformation, that utilized deep learning. The probability transfer matrix of the …
A weighted network community detection algorithm based on deep learning
S Li, L Jiang, X Wu, W Han, D Zhao, Z Wang - Applied Mathematics and …, 2021 - Elsevier
At present, community detection methods are mostly focused on the investigation at
unweighted networks. However, real-world networks are always complex, and unweighted …
unweighted networks. However, real-world networks are always complex, and unweighted …
Community detection algorithm based on nonnegative matrix factorization and improved density peak clustering
H Lu, X Sang, Q Zhao, J Lu - IEEE Access, 2020 - ieeexplore.ieee.org
Community detection is a critical issue in the field of complex networks. Recently, the
nonnegative matrix factorization (NMF) method has successfully uncovered the community …
nonnegative matrix factorization (NMF) method has successfully uncovered the community …
Multi-level learning based memetic algorithm for community detection
Complex network has become an important way to analyze the massive disordered
information of complex systems, and its community structure property is indispensable to …
information of complex systems, and its community structure property is indispensable to …
Autoencoder based community detection with adaptive integration of network topology and node contents
Community detection plays an important role in understanding the structure and laws of
social networks. Many community detection approaches have been proposed and focus on …
social networks. Many community detection approaches have been proposed and focus on …
A community structure enhancement-based community detection algorithm for complex networks
Y Su, C Liu, Y Niu, F Cheng… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Community detection has been recognized as one of the most important tools to discover
useful information hidden in complex networks which is usually hard to be obtained by …
useful information hidden in complex networks which is usually hard to be obtained by …
Estimating the similarity of community detection methods based on cluster size distribution
Detecting community structure discloses tremendous information about complex networks
and unlock promising applied perspectives. Accordingly, a numerous number of community …
and unlock promising applied perspectives. Accordingly, a numerous number of community …
VGHC: a variable granularity hierarchical clustering for community detection
J Chen, Y Li, X Yang, S Zhao, Y Zhang - Granular Computing, 2021 - Springer
Hierarchical clustering is an effective method for community detection. This kind of method
usually selects clustering threshold for layering, which will affect the performance of …
usually selects clustering threshold for layering, which will affect the performance of …