AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning

Yongqi Zhang, Zhanke Zhou, Quanming Yao*, Xiaowen Chu, Bo Han

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step. Existing methods use hand-designed propagation paths, ignoring the correlation between the entities and the query relation. In addition, the number of involved entities will explosively grow at larger propagation steps. In this work, we are motivated to learn an adaptive propagation path in order to filter out irrelevant entities while preserving promising targets. First, we design an incremental sampling mechanism where the nearby targets and layer-wise connections can be preserved with linear complexity. Second, we design a learning-based sampling distribution to identify the semantically related entities. Extensive experiments show that our method is powerful, efficient and semantic-aware. The code is available at https://github.com/LARS-research/AdaProp.

Original languageEnglish
Title of host publicationKDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery (ACM)
Pages3446-3457
Number of pages12
ISBN (Electronic)9798400701030
DOIs
Publication statusPublished - 4 Aug 2023
Event29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 - Long Beach, United States
Duration: 6 Aug 202310 Aug 2023
https://kdd.org/kdd2023/
https://dl.acm.org/doi/proceedings/10.1145/3580305

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023
Country/TerritoryUnited States
CityLong Beach
Period6/08/2310/08/23
Internet address

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • graph embedding
  • graph neural network
  • graph sampling
  • knowledge graph
  • knowledge graph reasoning

Fingerprint

Dive into the research topics of 'AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning'. Together they form a unique fingerprint.

Cite this