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Future Research Directions

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Heterogeneous Graph Representation Learning and Applications

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Abstract

Heterogeneous graph (HG) representation has made great progress in recent years, which clearly shows that it is a powerful and promising graph analysis paradigm. However, it is still a young and promising research field. In this chapter, we first make a summarization of this book and then illustrate some advanced topics, including challenging research issues, and explore a series of possible future research directions. One major potential direction is exploring fundamental ways to keep intrinsic structures or properties in HG. And another direction is to integrate the techniques widely used or newly emerged in machine learning to further enhance the applicability of HG on more key fields. We will illustrate more fine-grained potential works along with these two directions.

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Authors and Affiliations

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Chuan Shi & Xiao Wang

  2. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

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  1. Chuan Shi
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  2. Xiao Wang
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  3. Philip S. Yu
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Shi, C., Wang, X., Yu, P.S. (2022). Future Research Directions. 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_11

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  • DOI: https://doi.org/10.1007/978-981-16-6166-2_11

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  • Print ISBN: 978-981-16-6165-5

  • Online ISBN: 978-981-16-6166-2

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