Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Clustering is a fundamental machine learning task which has been widely studied in the
literature. Classic clustering methods follow the assumption that data are represented as …

Deep graph clustering, which aims to reveal the underlying graph structure and divide the
nodes into different groups, has attracted intensive attention in recent years. However, we …

In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …

Graph neural networks (GNNs) have achieved great success in many graph-based
applications. However, the enormous size and high sparsity level of graphs hinder their …

Graph anomaly detection, here, aims to find rare patterns that are significantly different from
other nodes. Attributed graphs containing complex structure and attribute information are …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Attributed graph clustering is a basic yet essential method for graph data exploration. Recent
efforts over graph contrastive learning have achieved impressive clustering performance …

Single-cell RNA sequencing (scRNA-seq) permits researchers to study the complex
mechanisms of cell heterogeneity and diversity. Unsupervised clustering is of central …

Multi-view clustering has become an active topic in artificial intelligence. Yet, similar
investigation for graph-structured data clustering has been absent so far. To fill this gap, we …