A comprehensive survey on community detection with deep learning
Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …
connections of a group of members that are different from those in other communities. The …
Machine learning algorithms for social media analysis: A survey
Social Media (SM) are the most widespread and rapid data generation applications on the
Internet increase the study of these data. However, the efficient processing of such massive …
Internet increase the study of these data. However, the efficient processing of such massive …
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 …
A novel link prediction algorithm for protein-protein interaction networks by attributed graph embedding
The prediction of interactions in protein networks is very critical in various biological
processes. In recent years, scientists have focused on computational approaches to predict …
processes. In recent years, scientists have focused on computational approaches to predict …
[HTML][HTML] A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information
Attributed graph clustering is a prominent research area, catering to the increasing need for
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks
Link prediction is one of the most widely studied problems in the area of complex network
analysis, in which machine learning techniques can be applied to deal with it. The biggest …
analysis, in which machine learning techniques can be applied to deal with it. The biggest …
Graph regularized nonnegative matrix factorization for community detection in attributed networks
K Berahmand, M Mohammadi… - … on Network Science …, 2022 - ieeexplore.ieee.org
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 …
A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix
The most basic and significant issue in complex network analysis is community detection,
which is a branch of machine learning. Most current community detection approaches, only …
which is a branch of machine learning. Most current community detection approaches, only …
Multiplex graph representation learning via dual correlation reduction
Recently, with the superior capacity for analyzing the multiplex graph data, self-supervised
multiplex graph representation learning (SMGRL) has received much interest. However …
multiplex graph representation learning (SMGRL) has received much interest. However …
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 …