Abstract
Networks (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.
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Shi, C., Wang, X., Yu, P.S. (2022). Introduction. In: Heterogeneous Graph Representation Learning and Applications. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-16-6166-2_1
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DOI: https://doi.org/10.1007/978-981-16-6166-2_1
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