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Found 5 papers, 5 papers with code
EGRC-Net: Embedding-induced Graph Refinement Clustering Network
To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation.
Deep Attention-guided Graph Clustering with Dual Self-supervision
Existing deep embedding clustering works only consider the deepest layer to learn a feature embedding and thus fail to well utilize the available discriminative information from cluster assignments, resulting performance limitation.
Adaptive Attribute and Structure Subspace Clustering Network
In this paper, we propose a novel adaptive attribute and structure subspace clustering network (AASSC-Net) to simultaneously consider the attribute and structure information in an adaptive graph fusion manner.
Attention-driven Graph Clustering Network
The combination of the traditional convolutional network (i. e., an auto-encoder) and the graph convolutional network has attracted much attention in clustering, in which the auto-encoder extracts the node attribute feature and the graph convolutional network captures the topological graph feature.
Maximum Entropy Subspace Clustering Network
Furthermore, we design a novel framework to explicitly decouple the auto-encoder module and the self-expressiveness module.
