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 survey of community detection methods in multilayer networks
X Huang, D Chen, T Ren, D Wang - Data Mining and Knowledge …, 2021 - Springer
Community detection is one of the most popular researches in a variety of complex systems,
ranging from biology to sociology. In recent years, there's an increasing focus on the rapid …
ranging from biology to sociology. In recent years, there's an increasing focus on the rapid …
Highly-accurate community detection via pointwise mutual information-incorporated symmetric non-negative matrix factorization
Community detection, aiming at determining correct affiliation of each node in a network, is a
critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non …
critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non …
WSNMF: Weighted symmetric nonnegative matrix factorization for attributed graph clustering
Abstract In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
LGIEM: Global and local node influence based community detection
Community detection is one of the hot topics in the complex networks. It aims to find
subgraphs that are internally dense but externally sparsely connected. In this paper, a new …
subgraphs that are internally dense but externally sparsely connected. In this paper, a new …
CC-GA: A clustering coefficient based genetic algorithm for detecting communities in social networks
A community structure is an integral part of a social network. Detecting such communities
plays an important role in a wide range of applications, including but not limited to cluster …
plays an important role in a wide range of applications, including but not limited to cluster …
A deep semi-supervised community detection based on point-wise mutual information
K Berahmand, Y Li, Y Xu - IEEE Transactions on Computational …, 2023 - ieeexplore.ieee.org
Network clustering is one of the fundamental unsupervised methods of knowledge
discovery. Its goal is to group similar nodes together without supervision or prior knowledge …
discovery. Its goal is to group similar nodes together without supervision or prior knowledge …
An alternating-direction-method of multipliers-incorporated approach to symmetric non-negative latent factor analysis
Large-scale undirected weighted networks are frequently encountered in big-data-related
applications concerning interactions among a large unique set of entities. Such a network …
applications concerning interactions among a large unique set of entities. Such a network …
Semi-supervised overlapping community detection in attributed graph with graph convolutional autoencoder
Community detection in attributed graph is of great application value and many related
methods have been continually presented. However, existing methods for community …
methods have been continually presented. However, existing methods for community …
Nonnegative matrix factorization with mixed hypergraph regularization for community detection
Community structure is the most significant attribute of networks, which is often identified to
help discover the underlying organization of networks. Currently, nonnegative matrix …
help discover the underlying organization of networks. Currently, nonnegative matrix …