Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

We investigate graph neural networks on graphs with heterophily. Some existing methods
amplify a node's neighborhood with multi-hop neighbors to include more nodes with …

Graph-based subspace clustering methods have exhibited promising performance.
However, they still suffer some of these drawbacks: they encounter the expensive time …

Multi-view graph clustering has been intensively investigated during the past years.
However, existing methods are still limited in two main aspects. On the one hand, most of …

The Internet-of-Things (IoT) technology is widely used in various fields. In the Earth
observation system, hyperspectral images (HSIs) are acquired by hyperspectral sensors and …

We present a novel deep neural network architecture for unsupervised subspace clustering.
This architecture is built upon deep auto-encoders, which non-linearly map the input data …

Multi-view clustering has attracted intensive attention due to the effectiveness of exploiting
multiple views of data. However, most existing multi-view clustering methods only aim to …

We propose a symmetric graph convolutional autoencoder which produces a low-
dimensional latent representation from a graph. In contrast to the existing graph …

This paper studies the subspace clustering problem. Given some data points approximately
drawn from a union of subspaces, the goal is to group these data points into their underlying …