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 …

Community detection methods based on attribute network representation learning are
receiving increasing attention. However, few existing works are focused exclusively on …

Attributed graph clustering, the task of grouping nodes into communities using both graph
structure and node attributes, is a fundamental problem in graph analysis. Recent …

Attributed graph clustering aims to group nodes into disjoint categories using deep learning
to represent node embeddings and has shown promising performance across various …

To learn the embedding representation of graph structure data corrupted by noise and
outliers, existing graph structure learning networks usually follow the two-step paradigm, ie …

In recent years, graph-based deep learning algorithms have attracted widespread attention
in the field of consumer electronics. Still, most of the current graph neural networks are …

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing
graph neural networks (GNNs) have shown impressive results. Despite the success of …

In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …

Community detection that tries to partition nodes with higher similarity in attributes and
topology structures into clusters has garnered substantial attention in the last several …

The graph convolutional network (GCN)-based clustering approaches have achieved the
impressive performance due to strong ability of exploiting the topological structure. The …