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 …

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 …

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 …

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph
analysis tasks such as node classification and link prediction. However, important …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, 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 …

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 …