Yijian Liu
ABSTRACT
In recent years, Heterogeneous Graph Neural Networks (HGNNs) have gained increasing attention due to their excellent performance in applications. However, the lack of high-quality benchmarks in new fields has become a critical limitation for developing and applying HGNNs. To accommodate the urgent need for emerging fields and the advancement of HGNNs, we present two large-scale, real-world, and challenging heterogeneous graph datasets from real scenarios: risk commodity detection and takeout recommendation. Meanwhile, we establish standard benchmark interfaces that provide over 40 heterogeneous graph datasets. We provide initial data split, unified evaluation metrics, and baseline results for future work, making it fair and handy to explore state-of-the-art HGNNs. Our interfaces also offer a comprehensive toolkit to research the characteristics of graph datasets. The above new datasets are publicly available on https://zenodo.org/communities/hgd, and the interface codes are available at https://github.com/BUPT-GAMMA/hgbi.
- Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Representation learning for attributed multiplex heterogeneous network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 1358--1368.Google Scholar
Digital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
- Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The Web Conference 2020. 2331--2341.Google ScholarDigital Library
- Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. 2020. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, Vol. 33 (2020), 21271--21284.Google Scholar
- Shuyun Gu, Xiao Wang, Chuan Shi, and Ding Xiao. 2022. Self-supervised Graph Neural Networks for Multi-behavior Recommendation. In International Joint Conference on Artificial Intelligence (IJCAI).Google Scholar
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017).Google Scholar
- Hui Han, Tianyu Zhao, Cheng Yang, Hongyi Zhang, Yaoqi Liu, Xiao Wang, and Chuan Shi. 2022. Openhgnn: an open source toolkit for heterogeneous graph neural network. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 3993--3997.Google ScholarDigital Library
- Binbin Hu, Yuan Fang, and Chuan Shi. 2019. Adversarial learning on heterogeneous information networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 120--129.Google ScholarDigital Library
- Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021. Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430 (2021).Google Scholar
- Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22118--22133.Google Scholar
- Houye Ji, Cheng Yang, Chuan Shi, and Pan Li. 2021b. Heterogeneous graph neural network with distance encoding. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 1138--1143.Google ScholarCross Ref
- Yugang Ji, Guanyi Chu, Xiao Wang, Chuan Shi, Jianan Zhao, and Junping Du. 2022. Prohibited Item Detection via Risk Graph Structure Learning. In WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, Fré dé rique Laforest, Raphaë l Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel Mé dini (Eds.). ACM, 1434--1443. https://doi.org/10.1145/3485447.3512190Google ScholarDigital Library
- Yugang Ji, Chuan Shi, and Xiao Wang. 2021a. Prohibited Item Detection on Heterogeneous Risk Graphs. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, Gianluca Demartini, Guido Zuccon, J. Shane Culpepper, Zi Huang, and Hanghang Tong (Eds.). ACM, 3867--3877. https://doi.org/10.1145/3459637.3481945Google ScholarDigital Library
- Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).Google Scholar
- Ao Li, Yugang Ji, Guanyi Chu, Xiao Wang, Dong Li, and Chuan Shi. 2023. Clustering-Based Supervised Contrastive Learning for Identifying Risk Items on Heterogeneous Graph. In ICASSP 2023--2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1--5.Google Scholar
- Jiawei Liu, Chuan Shi, Cheng Yang, Zhiyuan Lu, and S Yu Philip. 2022. A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources. AI Open, Vol. 3 (2022), 40--57.Google ScholarCross Ref
- Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, and Jie Tang. 2021. Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining. 1150--1160.Google Scholar
- M Marvi-Mashhadi, CS Lopes, and J LLorca. 2020. High fidelity simulation of the mechanical behavior of closed-cell polyurethane foams. Journal of the Mechanics and Physics of Solids, Vol. 135 (2020), 103814.Google ScholarCross Ref
- 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 The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, Proceedings 15. Springer, 593--607.Google ScholarDigital Library
- Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and Philip S. Yu. 2017. A survey of heterogeneous information network analysis. IEEE Transactions on Knowledge and Data Engineering, Vol. 29, 1 (2017), 17--37. https://doi.org/10.1109/TKDE.2016.2598561Google ScholarDigital Library
- Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, and Partha Talukdar. 2019. Composition-based multi-relational graph convolutional networks. arXiv preprint arXiv:1911.03082 (2019).Google Scholar
- Petar Velivc ković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).Google Scholar
- Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, et al. 2019b. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315 (2019).Google Scholar
- Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, and S Yu Philip. 2022. A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Transactions on Big Data (2022).Google Scholar
- Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019a. Heterogeneous graph attention network. In The world wide web conference. 2022--2032.Google Scholar
- Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020. Heterogeneous network representation learning: A unified framework with survey and benchmark. IEEE Transactions on Knowledge and Data Engineering, Vol. 34, 10 (2020), 4854--4873.Google ScholarDigital Library
- Tianchi Yang, Cheng Yang, Luhao Zhang, Chuan Shi, Maodi Hu, Huaijun Liu, Tao Li, and Dong Wang. 2022a. Co-clustering interactions via attentive hypergraph neural network. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 859--869.Google ScholarDigital Library
- Tianchi Yang, Luhao Zhang, Chuan Shi, Cheng Yang, Siyong Xu, Ruiyu Fang, Maodi Hu, Huaijun Liu, Tao Li, and Dong Wang. 2022b. Gated Hypergraph Neural Network for Scene-Aware Recommendation. In International Conference on Database Systems for Advanced Applications. Springer, 199--215.Google Scholar
- Yaming Yang, Ziyu Guan, Jianxin Li, Wei Zhao, Jiangtao Cui, and Quan Wang. 2021. Interpretable and efficient heterogeneous graph convolutional network. IEEE Transactions on Knowledge and Data Engineering (2021).Google ScholarCross Ref
- Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48.Google Scholar
- Lingfan Yu, Jiajun Shen, Jinyang Li, and Adam Lerer. 2020. Scalable graph neural networks for heterogeneous graphs. arXiv preprint arXiv:2011.09679 (2020).Google Scholar
- Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, S Yi Sean, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2022. Graph Transformer Networks: Learning meta-path graphs to improve GNNs. Neural Networks, Vol. 153 (2022), 104--119.Google ScholarDigital Library
- Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. 793--803.Google ScholarDigital Library
- Luhao Zhang, Ruiyu Fang, Tianchi Yang, Maodi Hu, Tao Li, Chuan Shi, and Dong Wang. 2022. A Joint Framework for Explainable Recommendation with Knowledge Reasoning and Graph Representation. In International Conference on Database Systems for Advanced Applications. Springer, 351--363.Google ScholarDigital Library
Index Terms
- Datasets and Interfaces for Benchmarking Heterogeneous Graph Neural Networks
Recommendations
Node-dependent Semantic Search over Heterogeneous Graph Neural Networks
CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge ManagementIn recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-art approaches for various tasks on Heterogeneous Graphs (HGs), e.g., recommendation and social network analysis. Despite the success of existing HGNNs, the ...
Personalised meta-path generation for heterogeneous graph neural networks
AbstractRecently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node ...
Multi-Aspect Heterogeneous Graph Augmentation
WWW '23: Proceedings of the ACM Web Conference 2023Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous ...
Comments