2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Bozhen Hu, Zelin Zang, Jun Xia +3
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent represent…
cs.LG2023
Dropout Attacks
Andrew Yuan, Alina Oprea, Cheng Tan
Dropout is a common operator in deep learning, aiming to prevent overfitting by randomly dropping neurons during training. This paper introduces a new family of poisoning attacks a…
cs.LG2023★ 2 cited
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
Xihong Yang, Cheng Tan, Yue Liu +7
Contrastive graph node clustering via learnable data augmentation is a hot research spot in the field of unsupervised graph learning. The existing methods learn the sampling distri…