A survey of community detection in complex networks using nonnegative matrix factorization
Community detection is one of the popular research topics in the field of complex networks
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …
analysis. It aims to identify communities, represented as cohesive subgroups or clusters …
[HTML][HTML] A review on community structures detection in time evolving social networks
N Alotaibi, D Rhouma - Journal of King Saud University-Computer and …, 2022 - Elsevier
The usage of social networks has widely increased in recent years. Humans tend to form
groups, in these networks, based on their similar interests. Such groups are known as …
groups, in these networks, based on their similar interests. Such groups are known as …
Robust orthogonal nonnegative matrix tri-factorization for data representation
Nonnegative matrix factorization (NMF) has been a vital data representation technique, and
has demonstrated significant potential in the field of machine learning and data mining …
has demonstrated significant potential in the field of machine learning and data mining …
Parallel multi-objective evolutionary optimization based dynamic community detection in software ecosystem
X Shen, X Yao, H Tu, D Gong - Knowledge-Based Systems, 2022 - Elsevier
Building a dynamic network in a software ecosystem and detecting its communities can not
only observe the structure of the dynamic network, but also reveal the evolution of these …
only observe the structure of the dynamic network, but also reveal the evolution of these …
PODCD: Probabilistic overlap** dynamic community detection
Community detection is an important task to reveal hidden structures of real-world complex
networks which are vary over time. Most of the existing works on the dynamic community …
networks which are vary over time. Most of the existing works on the dynamic community …
Identification of dynamic networks community by fusing deep learning and evolutionary clustering
Y Pan, X Liu, F Yao, L Zhang, W Li, P Wang - Scientific Reports, 2024 - nature.com
Community detection is a critical component of network analysis and a hot topic in social
computing. Detecting community structure in dynamic networks has important theoretical …
computing. Detecting community structure in dynamic networks has important theoretical …
Community reinforcement: An effective and efficient preprocessing method for accurate community detection
Existing community detection algorithms may be often unsatisfactory due to low detection
accuracy in real-world graphs since the number of edges between the nodes in the same …
accuracy in real-world graphs since the number of edges between the nodes in the same …
A motif-based probabilistic approach for community detection in complex networks
H Hajibabaei, V Seydi, A Koochari - Journal of Intelligent Information …, 2024 - Springer
Community detection in complex networks is an important task for discovering hidden
information in network analysis. Neighborhood density between nodes is one of the …
information in network analysis. Neighborhood density between nodes is one of the …
MK-Means: Detecting evolutionary communities in dynamic networks
YC Chen, YL Chen, JY Lu - Expert Systems with Applications, 2021 - Elsevier
K-Means algorithm is probably the most famous and popular clustering algorithm in the
world. K-Means algorithm has the advantages of simple structure, easy implementation, high …
world. K-Means algorithm has the advantages of simple structure, easy implementation, high …
A spiderweb model for community detection in dynamic networks
H Yang, J Cheng, X Su, W Zhang, S Zhao, X Chen - Applied Intelligence, 2021 - Springer
Community detection in dynamic networks is one of the most challenging tasks in the field of
network analysis. In general, networks often evolve smoothly between successive …
network analysis. In general, networks often evolve smoothly between successive …