Abstract
AbstractNetworks (or graphs) are ubiquitous in the real world, such as social networks, academic networks, biological networks, and so on. Heterogeneous information network (HIN), a.k.a., heterogeneous graph (HG), is an important type of network, which contains multiple types of nodes and edges. To date, the research of HG has attracted extensive attention, the most important of which is the heterogeneous graph representation (HGR), a.k.a., heterogeneous network embedding. In this chapter, we first introduce some basic concepts and definitions in HG and emphasize the importance of graph representation learning in the field of data mining. Then, we analyze the unique challenges of HGR compared with homogeneous network. In the end, we briefly introduce the organization of this book.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.