Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

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 data in the real world is often accompanied by the problem of missing attributes.
Recently, self-supervised graph representation learning, implementing data imputation …

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues
on graphs involve both attributes and topological structures. Existing work on fair graph …

Heterogeneous graph embedding is receiving increasing attention from researchers due to
the ubiquity of heterogeneous graphs (HGs). How to effectively handle the problem of …

Community detection aims to identify dense subgroups of nodes within a network. However,
in real-world networks, node attributes are often missing, making traditional methods less …

Attribute graphs are a crucial data structure for graph communities. However, the presence
of redundancy and noise in the attribute graph can impair the aggregation effect of …

Graph self-supervised representation learning has gained considerable attention and
demonstrated remarkable efficacy in extracting meaningful representations from graphs …

With the development of various applications, such as recommendation systems and social
network analysis, graph data have been ubiquitous in the real world. However, graphs …

In recent years, knowledge graph technology has been widely applied in various fields such
as intelligent auditing, urban transportation planning, legal research, and financial analysis …