A Deep Graph Structured Clustering Network
Graph clustering is a fundamental task in data analysis and has attracted considerable attention in recommendation systems, mapping knowledge domain, and biological science. Because graph convolution is very effective in combining the feature information and topology information of graph data, some...
Main Authors: | Xunkai Li, Youpeng Hu, Yaoqi Sun, Ji Hu, Jiyong Zhang, Meixia Qu |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9181620/ |
Similar Items
-
Nonlinear Subspace Clustering via Adaptive Graph Regularized Autoencoder
by: Qiang Ji, et al.
Published: (2019-01-01) -
Graph Convolution-Based Deep Clustering for Speech Separation
by: Shan Qin, et al.
Published: (2020-01-01) -
Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
by: Youngchun Kwon, et al.
Published: (2019-11-01) -
A Simple Graph Convolutional Network With Abundant Interaction for Collaborative Filtering
by: Ronghui Guo, et al.
Published: (2021-01-01) -
Deep Convolutional Clustering-Based Time Series Anomaly Detection
by: Gavneet Singh Chadha, et al.
Published: (2021-08-01)