Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

With the explosive growth of information technology, multi-view graph data have become
increasingly prevalent and valuable. Most existing multi-view clustering techniques either …

Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …

Multi-view graph clustering has been intensively investigated during the past years.
However, existing methods are still limited in two main aspects. On the one hand, most of …

Networks have been widely used to represent the relations between objects such as
academic networks and social networks, and learning embedding for networks has thus …

Since real-world objects and their interactions are often multi-modal and multi-typed,
heterogeneous networks have been widely used as a more powerful, realistic, and generic …

Nodes in a multiplex network are connected by multiple types of relations. However, most
existing network embedding methods assume that only a single type of relation exists …

An increasing number of machine learning tasks require dealing with large graph datasets,
which capture rich and complex relationship among potentially billions of elements. Graph …

Multi-view graph clustering, which seeks a partition of the graph with multiple views that
often provide more comprehensive yet complex information, has received considerable …