skip to main content
10.1145/3488560.3502219acmconferencesArticle/Chapter ViewAbstractPublication PageswsdmConference Proceedingsconference-collections
extended-abstract

From Uni-relational to Multi-relational Graph Neural Networks

Published: 15 February 2022 Publication History

Abstract

Graph Neural Networks (GNNs), which extend deep neural networks to graph-structured data, have attracted increasing attention. They have been proven to be powerful for numerous graph related tasks that cover a variety of research areas including natural language processing, information retrieval and knowledge graph completion (KGC). GNNs are primary designed for simple homogeneous and uni-relational graphs. Due to its great success in handling the graph data, considerable studies have been developed to extend GNNs to process complex multi-relational graphs such as the knowledge graph. My research first focuses on learning effective representation of uni-relational graph to facilitate some downstream applications such as graph classification and query understanding, and show the great capacity of GNNs to advance these tasks. Although the GNNs have demonstrated its significant effectiveness on the uni-relational graph in a large range of applications, we surprisingly found it may not be as crucial as previously believed in the knowledge graph completion task. It suggests careful attention to more suitable GNNs designs for KGC task.

References

[1]
Lars Backstrom and Jure Leskovec. 2011. Supervised random walks: predicting and recommending links in social networks. In Proceedings of the fourth ACM international conference on Web search and data mining. 635--644.
[2]
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data . 1247--1250.
[3]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2--7, 2019, Volume 1 (Long and Short Papers). 4171--4186.
[4]
Hongyang Gao and Shuiwang Ji. 2019. Graph U-Nets. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9--15 June 2019, Long Beach, California, USA. 2083--2092.
[5]
William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025--1035.
[6]
Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24--26, 2017, Conference Track Proceedings .
[7]
Juanhui Li, Yao Ma, Yiqi Wang, Charu Aggarwal, Chang-Dong Wang, and Jiliang Tang. 2020. Graph Pooling with Representativeness. In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 302--311.
[8]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Twenty-ninth AAAI conference on artificial intelligence .
[9]
Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2019. Graph convolutional networks with eigenpooling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 723--731.
[10]
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In European Semantic Web Conference . Springer, 593--607.
[11]
Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha P. Talukdar. 2020. Composition-based Multi-Relational Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020. OpenReview.net.
[12]
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations .
[13]
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018a. Graph convolutional neural networks for web-scale recommender systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 974--983.
[14]
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018b. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810.
[15]
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434 (2018).

Cited By

View all
  • (2023)A subgraph interpretation generative model for knowledge graph link prediction based on uni-relation transformationFifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)10.1117/12.3009388(55)Online publication date: 16-Oct-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
February 2022
1690 pages
ISBN:9781450391320
DOI:10.1145/3488560
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 February 2022

Check for updates

Author Tags

  1. graph classification
  2. graph neural networks
  3. graph pooling
  4. knowledge graph
  5. query understanding

Qualifiers

  • Extended-abstract

Funding Sources

  • Army Research Office (ARO)
  • National Science Foundation

Conference

WSDM '22

Acceptance Rates

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)21
  • Downloads (Last 6 weeks)1
Reflects downloads up to 13 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)A subgraph interpretation generative model for knowledge graph link prediction based on uni-relation transformationFifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)10.1117/12.3009388(55)Online publication date: 16-Oct-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media