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Found 5 papers, 4 papers with code
Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs.
A Contrastive Variational Graph Auto-Encoder for Node Clustering
Thanks to a newly identified term, our lower bound can escape Posterior Collapse and has more flexibility to account for the difference between the inference and generative models.
Rethinking Graph Auto-Encoder Models for Attributed Graph Clustering
We study these issues from two aspects: (1) there is a trade-off between Feature Randomness and Feature Drift when clustering and reconstruction are performed at the same level, and (2) the problem of Feature Drift is more pronounced for GAE models, compared with vanilla auto-encoder models, due to the graph convolutional operation and the graph decoding design.
Ranked #1 on
Node Clustering
on Pubmed
Adversarial Deep Embedded Clustering: on a better trade-off between Feature Randomness and Feature Drift
Clustering using deep autoencoders has been thoroughly investigated in recent years.
Ranked #2 on
Image Clustering
on MNIST-full
Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities.
Ranked #1 on
Image Clustering
on MNIST-test
