Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Disease–gene association through genome-wide association study (GWAS) is an arduous
task for researchers. Investigating single nucleotide polymorphisms that correlate with …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

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 …

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 …

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 …

Graph clustering has become an important research topic due to the proliferation of graph
data. However, existing methods suffer from two major drawbacks. On the one hand, most …

Graph representation learning is crucial for many real-world applications (eg social relation
analysis). A fundamental problem for graph representation learning is how to effectively …