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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 …
Community detection algorithms in healthcare applications: A systematic review
Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
Graph regularized nonnegative matrix factorization for community detection in attributed networks
Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …
the proliferation of network data. However, most existing methods have been developed …
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 …
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 …
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 …
Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection
Community is a fundamental and highly desired pattern in a Large-scale Undirected
Network (LUN). Community detection is a vital issue when LUN representation learning is …
Network (LUN). Community detection is a vital issue when LUN representation learning is …
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 …
Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data
We propose a novel approach for isolating customer segments using online customer data
for products that are distributed via online social media platforms. We use non-negative …
for products that are distributed via online social media platforms. We use non-negative …
Semisupervised adaptive symmetric non-negative matrix factorization
As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can
generate the clustering result without additional post-processing, by decomposing a …
generate the clustering result without additional post-processing, by decomposing a …