Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

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 …

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

Attributed networks are ubiquitous and form a critical component of modern information
infrastructure, where additional node attributes complement the raw network structure in …

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 …

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 …

Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured
data, have drawn considerable attention and achieved state-of-the-art performance in …

Graph convolutional networks (GCNs) have achieved impressive success in many graph
related analytics tasks. However, most GCNs only work in a single domain (graph) …