The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks
Abstract
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements. In this work, we present a systematical reproduction of 12 recent HGNNs by using their official codes, datasets, settings, and hyperparameters, revealing surprising findings about the progress of HGNNs. We find that the simple homogeneous GNNs, e.g., GCN and GAT, are largely underestimated due to improper settings. GAT with proper inputs can generally match or outperform all existing HGNNs across various scenarios. To facilitate robust and reproducible HGNN research, we construct the Heterogeneous Graph Benchmark (HGB) , consisting of 11 diverse datasets with three tasks. HGB standardizes the process of heterogeneous graph data splits, feature processing, and performance evaluation. Finally, we introduce a simple but very strong baseline Simple-HGN-which significantly outperforms all previous models on HGB-to accelerate the advancement of HGNNs in the future.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513
Heterogeneous Graph Attention Network
Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., & Yu, P. S. (2019) · The World Wide Web Conference · doi:10.1145/3308558.3313562 · Cited by 3,191
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
Li, Q., Han, Z., & Wu, X.-M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11604 · Cited by 2,872
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Dong, Y., Chawla, N. V., & Swami, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098036 · Cited by 2,210
Self-citationKGAT: Knowledge Graph Attention Network for Recommendation
Wang, X., He, X., Cao, Y., Liu, M., & Chua, T.-S. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330989 · Cited by 2,147
Metadata
- Authors
- Lv, Q., Ding, M., Liu, Q., Chen, Y., Feng, W., He, S., Zhou, C., Jiang, J., Dong, Y., & Tang, J.
- Venue
- Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2021-08-14
- Pages
- 1150-1160
- OpenAlex
- W4285378361
- Cited by
- 292
- References
- 26
- FWCI
- 18.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lv, Q., Ding, M., Liu, Q., Chen, Y., Feng, W., He, S., Zhou, C., Jiang, J., Dong, Y., & Tang, J. (2021). Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3447548.3467350
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
