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OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network

Published: 17 October 2022 Publication History

Abstract

Heterogeneous Graph Neural Networks (HGNNs), as a kind of powerful graph representation learning methods on heterogeneous graphs, have attracted increasing attention of many researchers. Although, several existing libraries have supported HGNNs, they just provide the most basic models and operators. Building and benchmarking various downstream tasks on HGNNs is still painful and time consuming with them. In this paper, we will introduce OpenHGNN, an open-source toolkit for HGNNs. OpenHGNN defines a unified and standard pipeline for training and testing, which can allow users to run a model on a specific dataset with just one command line. OpenHGNN has integrated 20+ mainstream HGNNs and 20+ heterogeneous graph datasets, which can be used for various advanced tasks, such as node classification, link prediction, and recommendation. In addition, thanks to the modularized design of OpenHGNN, it can be extended to meet users' customized needs. We also release several novel and useful tools and features, including leaderboard, autoML, design space, and visualization, to provide users with better usage experiences. OpenHGNN is an open-source project, and the source code is available at https://github.com/BUPT-GAMMA/OpenHGNN.

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cover image ACM Conferences
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
October 2022
5274 pages
ISBN:9781450392365
DOI:10.1145/3511808
  • General Chairs:
  • Mohammad Al Hasan,
  • Li Xiong
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 17 October 2022

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Author Tags

  1. frameworks
  2. heterogeneous graph neural networks
  3. heterogeneous graph representation learning

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Cited By

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  • (2025)Preserving high-order ego-centric topological patterns in node representation in heterogeneous graphKnowledge-Based Systems10.1016/j.knosys.2025.113067311(113067)Online publication date: Feb-2025
  • (2024)Graph Attention Networks: A Comprehensive Review of Methods and ApplicationsFuture Internet10.3390/fi1609031816:9(318)Online publication date: 3-Sep-2024
  • (2024)BSIN: A Behavior Schema of Information Networks Based on Approximate BisimulationTsinghua Science and Technology10.26599/TST.2023.901008129:4(1092-1104)Online publication date: Aug-2024
  • (2024)Heterogeneous graph transformer with poly-tokenizationProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/247(2234-2242)Online publication date: 3-Aug-2024
  • (2024)Denoising Heterogeneous Graph Pre-training Framework for RecommendationACM Transactions on Information Systems10.1145/3706632Online publication date: 5-Dec-2024
  • (2024)Heterogeneous GNN with Express Edges for Intrusion Detection in Cyber-Physical Systems2024 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC59896.2024.10556029(523-529)Online publication date: 19-Feb-2024
  • (2024)Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00148(1833-1846)Online publication date: 13-May-2024
  • (2024)H-BERT4Rec: Enhancing Sequential Recommendation System on MOOCs Based on Heterogeneous Information NetworksIEEE Access10.1109/ACCESS.2024.346283012(155789-155803)Online publication date: 2024
  • (2024)IC points weight learning-based GCN and improving feature distribution for industrial fault diagnosisExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124681255:PCOnline publication date: 1-Dec-2024
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