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 …
Deep learning for community detection: progress, challenges and opportunities
As communities represent similar opinions, similar functions, similar purposes, etc.,
community detection is an important and extremely useful tool in both scientific inquiry and …
community detection is an important and extremely useful tool in both scientific inquiry and …
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 …
Deep learning techniques for community detection in social networks
Graph embedding is an effective yet efficient way to convert graph data into a low
dimensional space. In recent years, deep learning has applied on graph embedding and …
dimensional space. In recent years, deep learning has applied on graph embedding and …
Overlapping community detection with graph neural networks
O Shchur, S Günnemann - arXiv preprint arXiv:1909.12201, 2019 - arxiv.org
Community detection is a fundamental problem in machine learning. While deep learning
has shown great promise in many graphrelated tasks, developing neural models for …
has shown great promise in many graphrelated tasks, developing neural models for …
Sdac-da: Semi-supervised deep attributed clustering using dual autoencoder
Attributed graph clustering aims to group nodes into disjoint categories using deep learning
to represent node embeddings and has shown promising performance across various …
to represent node embeddings and has shown promising performance across various …
A survey about community detection over On-line Social and Heterogeneous Information Networks
Abstract In modern Online Social Networks (OSNs), the need to detect users' communities
based on their interests and social connections has became a more and more important …
based on their interests and social connections has became a more and more important …
Unsupervised learning for community detection in attributed networks based on graph convolutional network
X Wang, J Li, L Yang, H Mi - Neurocomputing, 2021 - Elsevier
Community detection has emerged during the last decade as one of the most challenging
problems in network science, which has been revisited with network representation learning …
problems in network science, which has been revisited with network representation learning …
Characterizing the fuzzy community structure in link graph via the likelihood optimization
Detecting fuzzy communities in networks is a critical but challenging task in many fields
including biology, technology, social system and so on. Current technology is largely reliant …
including biology, technology, social system and so on. Current technology is largely reliant …