A survey of community detection approaches: From statistical modeling to deep learning
Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …
into multiple sub-structures to help reveal their latent functions. Community detection has …
A review on semi-supervised clustering
J Cai, J Hao, H Yang, X Zhao, Y Yang - Information Sciences, 2023 - Elsevier
Abstract Semi-supervised clustering (SSC), a technique integrating semi-supervised
learning and clustering analysis, incorporates the given prior information (eg, class labels …
learning and clustering analysis, incorporates the given prior information (eg, class labels …
Symmetric nonnegative matrix factorization-based community detection models and their convergence analysis
Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …
Structure and inference in annotated networks
For many networks of scientific interest we know both the connections of the network and
information about the network nodes, such as the age or gender of individuals in a social …
information about the network nodes, such as the age or gender of individuals in a social …
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 …
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 …
SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering
Nonnegative matrix factorization (NMF) provides a lower rank approximation of a matrix by a
product of two nonnegative factors. NMF has been shown to produce clustering results that …
product of two nonnegative factors. NMF has been shown to produce clustering results that …
A unified semi-supervised community detection framework using latent space graph regularization
Community structure is one of the most important properties of complex networks and is a
foundational concept in exploring and understanding networks. In real world, topology …
foundational concept in exploring and understanding networks. In real world, topology …
Community detection in multi-layer networks using joint nonnegative matrix factorization
Many complex systems are composed of coupled networks through different layers, where
each layer represents one of many possible types of interactions. A fundamental question is …
each layer represents one of many possible types of interactions. A fundamental question is …
A deep semi-supervised community detection based on point-wise mutual information
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 …