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 …

Deep clustering with attribute-missing graphs, where only a subset of nodes possesses
complete attributes while those of others are missing, is an important yet challenging topic in …

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 …

This survey article presents a comprehensive and conceptual overview of anomaly detection
(AD) using dynamic graphs. We focus on existing graph-based AD techniques and their …

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised
learning on graphs, which generally follows the" augmenting-contrasting''learning scheme …

Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional
spaces and have become the de-facto standard for knowledge graph completion. Most …

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks
in an unsupervised manner, has attracted great attention in recent years. Although the …

Graph-structured data, prevalent in domains ranging from social networks to biochemical
analysis, serve as the foundation for diverse real-world systems. While graph neural …

In recent years, deep clustering has achieved encouraging results. However, existing deep
clustering methods work with the traditional Euclidean space and thus present deficiency on …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …