Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness
a strong tendency of combining autoencoder and graph neural networks to exploit structure …

In the first days of social networking, the typical view of a community was a set of user
profiles of the same interests and likes, and this community kept enlarging by searching …

Despite the promising preliminary results, existing graph convolutional network (GCN)
based multi-view learning methods directly use the graph structure as view descriptor, which …

The combination of the traditional convolutional network (ie, an auto-encoder) and the graph
convolutional network has attracted much attention in clustering, in which the auto-encoder …

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views,
has received considerable attention in recent years. Although great efforts have been made …

Graph-based clustering plays an important role in the clustering area. Recent studies about
graph neural networks (GNN) have achieved impressive success on graph-type data …

Attributed graph clustering aims to discover node groups by utilizing both graph structure
and node features. Recent studies mostly adopt graph neural networks to learn node …

Face clustering has important applications in image retrieval and criminal investigation.
Face images can be seen as the nodes of a graph and the possibility of links between the …

Community detection is of great help to understand the structures and functions of complex
networks. It has become one of popular research topics in the field of complex networks …

In recent years, incomplete multi-view clustering has drawn increasing attention due to the
existence of large amounts of unlabeled incomplete data whose views are not fully observed …