Representation learning in heterogeneous graphs (HG) is intended to
provide a meaningful vector representation for each node so as to
facilitate downstream applications such as link prediction, personalized
recommendation, node classification, etc. This task, however, is
challenging not only because of the need to incorporate heterogeneous
structural (graph) information consisting of multiple types of node and
edge, but also the need to consider heterogeneous attributes or types of
content (e.g. text or image) associated with each node. Although
considerable advances have been made in homogeneous (and heterogeneous)
graph embedding, attributed graph embedding and graph neural networks,
few are capable of simultaneously and effectively taking into account
heterogeneous structural (graph) information as well as the
heterogeneous content information of each node.
In this book, we provide a comprehensive survey of current developments
in HG representation learning. More importantly, we present the
state-of-the-art in this field, including theoretical models and real
applications that have been showcased at the top conferences and
journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major
objectives: (1) to provide researchers with an understanding of the
fundamental issues and a good point of departure for working in this
rapidly expanding field, and (2) to present the latest research on
applying heterogeneous graphs to model real systems and learning
structural features of interaction systems. To the best of our
knowledge, it is the first book to summarize the latest developments and
present cutting-edge research on heterogeneous graph representation
learning. To gain the most from it, readers should have a basic grasp of
computer science, data mining and machine learning.
