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 …
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 …
Attributed graph clustering via adaptive graph convolution
Attributed graph clustering is challenging as it requires joint modelling of graph structures
and node attributes. Recent progress on graph convolutional networks has proved that …
and node attributes. Recent progress on graph convolutional networks has proved that …
Karate Club: an API oriented open-source python framework for unsupervised learning on graphs
Graphs encode important structural properties of complex systems. Machine learning on
graphs has therefore emerged as an important technique in research and applications. We …
graphs has therefore emerged as an important technique in research and applications. We …
Gemsec: Graph embedding with self clustering
B Rozemberczki, R Davies, R Sarkar… - Proceedings of the 2019 …, 2019 - dl.acm.org
Modern graph embedding procedures can efficiently process graphs with millions of nodes.
In this paper, we propose GEMSEC-a graph embedding algorithm which learns a clustering …
In this paper, we propose GEMSEC-a graph embedding algorithm which learns a clustering …
Hyperspectral unmixing based on nonnegative matrix factorization: A comprehensive review
Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
endmembers and their corresponding abundances from a hyperspectral image (HSI) …
Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction
G D'Angelo, F Palmieri - Journal of Network and Computer Applications, 2021 - Elsevier
The right choice of features to be extracted from individual or aggregated observations is an
extremely critical factor for the success of modern network traffic classification approaches …
extremely critical factor for the success of modern network traffic classification approaches …
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 …
A survey of deep nonnegative matrix factorization
WS Chen, Q Zeng, B Pan - Neurocomputing, 2022 - Elsevier
Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
feature extraction in recent years. By decomposing the matrix recurrently on account of the …
Fast Markov clustering algorithm based on belief dynamics
Graph clustering is one of the most significant, challenging, and valuable topic in the
analysis of real complex networks. To detect the cluster configuration accurately and …
analysis of real complex networks. To detect the cluster configuration accurately and …