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Dynamic Heterogeneous Graph Representation

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Heterogeneous Graph Representation Learning and Applications

Abstract

Graphs are gradually generated with multiple temporal heterogeneous interactions in real-world scenarios, containing both abundant structures and complex dynamics. Compared to static heterogeneous graphs, the dynamics express not only the changing graph topology but also the sequential evolution as well as multiple temporal preferences, indicating the necessity of dynamic heterogeneous graph modeling. This chapter focuses on simultaneously modeling both the evolving dynamics and heterogeneous semantics, and introduces three representative approaches, including DyHNE to handle structure changes via matrix perturbation theory based incremental learning, SHCF to tackle evolving sequences via heterogeneous sequential neural collaborative filtering, and THIGE to model multiple long- and short-term preferences via temporal heterogeneous GNNs.

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Notes

  1. 1.

    According to our experiments, fine-grained and dynamic user interest modeling layer is taken as the L-th layer for user modeling.

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Authors and Affiliations

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

    Chuan Shi

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

    Xiao Wang

  3. University of Illinois at Chicago, Department of Computer Science, Chicago, 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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Correspondence to Chuan Shi , Xiao Wang or Philip S. Yu .

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Shi, C., Wang, X., S. Yu, P. (2022). Dynamic Heterogeneous Graph Representation. 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_5

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  • DOI: https://doi.org/10.1007/978-981-16-6166-2_5

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