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Attribute-Assisted Heterogeneous Graph Representation

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Heterogeneous Graph Representation Learning and Applications

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Abstract

The previous heterogeneous graph representation methods mainly focus on preserving the complex interactions and rich semantics into node representation. As a matter of fact, diverse types of nodes in heterogeneous graph are assisted with different attributes, providing valuable side information for depicting the characteristics of nodes. Integrating attribution information is also desired for heterogeneous graph representation in a real-world application. Fortunately, heterogeneous graph neural networks naturally provide an alternative way to achieve this, meanwhile, have powerful representation ability. In this chapter, we introduce three attribute-assisted heterogeneous graph representation models including heterogeneous graph attention network (HAN), heterogeneous graph propagation network (HPN), and heterogeneous graph structure learning (HGSL), which simultaneously utilize both complex structural information and rich attribute information to learn node representation.

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Notes

  1. 1.

    https://dblp.uni-trier.de.

  2. 2.

    http://dl.acm.org/.

  3. 3.

    https://www.kaggle.com/carolzhangdc/imdb-5000-moviedataset.

  4. 4.

    https://github.com/Jhy1993/HAN.

  5. 5.

    Xintao Wu, Daniel Barbara, Yong Ye. Screening and Interpreting Multi-item Associations Based on Log-linear Modeling, KDD’03.

  6. 6.

    Xintao Wu, Jianpin Fan, Kalpathi Subramanian. B-EM: a classifier incorporating bootstrap with EM approach for data mining, KDD’02.

  7. 7.

    Daniel Barbara, Carlotta Domeniconi, James P. Rogers. Detecting outliers using transduction and statistical testing, KDD’06.

  8. 8.

    Walid G. Aref, Daniel Barbara, Padmavathi Vallabhaneni. The Handwritten Trie: Indexing Electronic Ink, SIGMOD’95.

  9. 9.

    Daniel Barbara, Tomasz Imielinski. Sleepers and Workaholics: Caching Strategies in Mobile Environments, VLDB’95.

  10. 10.

    Hector Garcia-Holina, Daniel Barbara. The cost of data replication, SIGCOMM’81.

  11. 11.

    https://www.yelp.com.

  12. 12.

    http://dl.acm.org/.

  13. 13.

    https://www.kaggle.com/carolzhangdc/imdb-5000-moviedataset.

  14. 14.

    https://grouplens.org/datasets/movielens/.

  15. 15.

    https://www.yelp.com.

  16. 16.

    http://dl.acm.org/.

  17. 17.

    https://dblp.uni-trier.de.

  18. 18.

    https://github.com/Andy-Border/HGSL.

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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). Attribute-Assisted 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_4

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  • DOI: https://doi.org/10.1007/978-981-16-6166-2_4

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