GE2: A General and Efficient Knowledge Graph Embedding Learning System (2024)

Antoine Bordes, Nicolas Usunier, Alberto Garc'i a-Durá n, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In Annual Conference on Neural Information Processing Systems 2013. December 5--8, 2013, Lake Tahoe, Nevada, United States. 2787--2795. https://proceedings.neurips.cc/paper/2013/hash/1cecc7a77928ca8133fa24680a88d2f9-Abstract.html

Liwei Cai and William Yang Wang. 2018. KBGAN: Adversarial Learning for Knowledge Graph Embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1--6, 2018, Volume 1 (Long Papers). Association for Computational Linguistics, 1470--1480. https://doi.org/10.18653/v1/n18--1133

Dawei Cheng, Fangzhou Yang, Xiaoyang Wang, Ying Zhang, and Liqing Zhang. 2020. Knowledge Graph-based Event Embedding Framework for Financial Quantitative Investments. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020. ACM, 2221--2230. https://doi.org/10.1145/3397271.3401427

Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, and Depeng Jin. 2020. Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. In Annual Conference on Neural Information Processing Systems 2020, December 6--12, 2020, virtual. https://proceedings.neurips.cc/paper/2020/hash/0c7119e3a6a2209da6a5b90e5b5b75bd-Abstract.html

Bi'an Du, Xiang Gao, Wei Hu, and Xin Li. 2021. Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning. In MM '21: ACM Multimedia Conference, Virtual Event, China, October 20 - 24, 2021. ACM, 3133--3142. https://doi.org/10.1145/3474085.3475458

Wei Duan, Junyu Xuan, Maoying Qiao, and Jie Lu. 2022. Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022. AAAI Press, 6550--6558. https://doi.org/10.1609/aaai.v36i6.20608

John C. Duchi, Elad Hazan, and Yoram Singer. 2010. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. In COLT 2010 - The 23rd Conference on Learning Theory, Haifa, Israel, June 27--29, 2010. Omnipress, 257--269. http://colt2010.haifa.il.ibm.com/papers/COLT2010proceedings.pdf#page=265

Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. CoRR, Vol. abs/1903.02428 (2019). showeprint[arXiv]1903.02428 http://arxiv.org/abs/1903.02428

Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3: Distributed Deep Graph Learning at Scale. In 15th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2021, July 14--16, 2021. USENIX Association, 551--568. https://www.usenix.org/conference/osdi21/presentation/gandhi

Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. In Annual Conference on Neural Information Processing Systems 2014, December 8--13 2014, Montreal, Quebec, Canada. 2672--2680. https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html

Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. ACM, 855--864. https://doi.org/10.1145/2939672.2939754

William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA. 1024--1034. https://proceedings.neurips.cc/paper/2017/hash/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html

Haim Hanani. 1961. The existence and construction of balanced incomplete block designs. The Annals of Mathematical Statistics, Vol. 32, 2 (1961), 361--386.

Haim Hanani, Dwijendra K Ray-Chaudhuri, and Richard M Wilson. 1972. On resolvable designs. Discrete Mathematics, Vol. 3, 4 (1972), 343--357.

David Hilbert and David Hilbert. 1935. Über die stetige Abbildung einer Linie auf ein Fl"achenstück. Dritter Band: Analysis· Grundlagen der Mathematik· Physik Verschiedenes: Nebst Einer Lebensgeschichte (1935), 1--2.

Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, and Jie Tang. 2021. MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14--18, 2021. ACM, 665--674. https://doi.org/10.1145/3447548.3467408

Ihab F. Ilyas, JP Lacerda, Yunyao Li, Umar Farooq Minhas, Ali Mousavi, Jeffrey Pound, Theodoros Rekatsinas, and Chiraag Sumanth. 2023. Growing and Serving Large Open-domain Knowledge Graphs. In Companion of the 2023 International Conference on Management of Data, SIGMOD/PODS 2023, Seattle, WA, USA, June 18--23, 2023, Sudipto Das, Ippokratis Pandis, K. Selcc uk Candan, and Sihem Amer-Yahia (Eds.). ACM, 253--259. https://doi.org/10.1145/3555041.3589672

Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. In Proceedings of Machine Learning and Systems 2020, MLSys 2020, Austin, TX, USA, March 2--4, 2020. mlsys.org. https://proceedings.mlsys.org/book/300.pdf

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. OpenReview.net. https://openreview.net/forum?id=SJU4ayYgl

Thomas P Kirkman. 1847. On a problem in combinations. Cambridge and Dublin Mathematical Journal, Vol. 2 ( 1847), 191--204.

Adrian Kochsiek and Rainer Gemulla. 2021. Parallel Training of Knowledge Graph Embedding Models: A Comparison of Techniques. Proc. VLDB Endow., Vol. 15, 3 (2021), 633--645. https://doi.org/10.14778/3494124.3494144

Adrian Kochsiek, Fritz Niesel, and Rainer Gemulla. 2022. Start small, think big: On hyperparameter optimization for large-scale knowledge graph embeddings. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 138--154.

Jonathan Larson, Darren Edge, Nathan Evans, and Christopher M. White. 2020. Making Sense of Search: Using Graph Embedding and Visualization to Transform Query Understanding. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI 2020, Honolulu, HI, USA, April 25--30, 2020. ACM, 1--8. https://doi.org/10.1145/3334480.3375233

Adam Lerer, Ledell Wu, Jiajun Shen, Timothé e Lacroix, Luca Wehrstedt, Abhijit Bose, and Alexander Peysakhovich. 2019. PyTorch-BigGraph: A Large-scale Graph Embedding System. CoRR, Vol. abs/1903.12287 (2019). showeprint[arXiv]1903.12287 http://arxiv.org/abs/1903.12287

Jure Leskovec. 2018. Tutorial: Representation Learning on Networks. http://snap.stanford.edu/proj/embeddings-www/

Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25--30, 2015, Austin, Texas, USA, Blai Bonet and Sven Koenig (Eds.). AAAI Press, 2181--2187. https://doi.org/10.1609/aaai.v29i1.9491

Zhiqi Lin, Cheng Li, Youshan Miao, Yunxin Liu, and Yinlong Xu. 2020. PaGraph: Scaling GNN training on large graphs via computation-aware caching. In SoCC '20: ACM Symposium on Cloud Computing, Virtual Event, USA, October 19--21, 2020. ACM, 401--415. https://doi.org/10.1145/3419111.3421281

Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, Hongzhi Chen, and Chuanxiong Guo. 2023. BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing. In 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, Boston, MA, April 17--19, 2023. USENIX Association, 103--118. https://www.usenix.org/conference/nsdi23/presentation/liu-tianfeng

Finlay MacLean. 2021. Knowledge graphs and their applications in drug discovery. Expert opinion on drug discovery, Vol. 16, 9 (2021), 1057--1069.

Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agü era y Arcas. 2017. Communication-Efficient Learning of Deep Networks from Decentralized Data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20--22 April 2017, Fort Lauderdale, FL, USA (Proceedings of Machine Learning Research, Vol. 54). PMLR, 1273--1282. http://proceedings.mlr.press/v54/mcmahan17a.html

Xupeng Miao, Hailin Zhang, Yining Shi, Xiaonan Nie, Zhi Yang, Yangyu Tao, and Bin Cui. 2021. HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework. Proc. VLDB Endow., Vol. 15, 2 (2021), 312--320. https://doi.org/10.14778/3489496.3489511

Tomá s Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5--8, 2013, Lake Tahoe, Nevada, United States, Christopher J. C. Burges, Lé on Bottou, Zoubin Ghahramani, and Kilian Q. Weinberger (Eds.). 3111--3119. https://proceedings.neurips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html

Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, and Shivaram Venkataraman. 2021. Marius: Learning Massive Graph Embeddings on a Single Machine. In 15th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2021, July 14--16, 2021. USENIX Association, 533--549. https://www.usenix.org/conference/osdi21/presentation/mohoney

Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. In Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011. Omnipress, 809--816. https://icml.cc/2011/papers/438_icmlpaper.pdf

Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. 2008. One-Class Collaborative Filtering. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), December 15--19, 2008, Pisa, Italy. IEEE Computer Society, 502--511. https://doi.org/10.1109/ICDM.2008.16

Dae Hoon Park and Yi Chang. 2019. Adversarial Sampling and Training for Semi-Supervised Information Retrieval. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13--17, 2019. ACM, 1443--1453. https://doi.org/10.1145/3308558.3313416

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: online learning of social representations. In The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014. ACM, 701--710. https://doi.org/10.1145/2623330.2623732

Jinfeng Rao, Hua He, and Jimmy Lin. 2016. Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks. In Proceedings of the 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, IN, USA, October 24--28, 2016. ACM, 1913--1916. https://doi.org/10.1145/2983323.2983872

Colin Reid and Alex Rosa. 2012. Steiner systems $ S (2, 4, v) $-a survey. The Electronic Journal of Combinatorics (2012), DS18--Feb.

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In UAI 2009, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, Montreal, QC, Canada, June 18--21, 2009. AUAI Press, 452--461. https://www.auai.org/uai2009/papers/UAI2009_0139_48141db02b9f0b02bc7158819ebfa2c7.pdf

Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net. https://openreview.net/forum?id=HkgEQnRqYQ

Zequn Sun, Wei Hu, Qingheng Zhang, and Yuzhong Qu. 2018a. Bootstrapping Entity Alignment with Knowledge Graph Embedding. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13--19, 2018, Stockholm, Sweden. ijcai.org, 4396--4402. https://doi.org/10.24963/ijcai.2018/611

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon, Long-Kai Huang, and Chi Xu. 2018b. Recurrent knowledge graph embedding for effective recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, October 2--7, 2018. ACM, 297--305. https://doi.org/10.1145/3240323.3240361

Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In Proceedings of the 24th International Conference on World Wide Web, WWW 2015, Florence, Italy, May 18--22, 2015. ACM, 1067--1077. https://doi.org/10.1145/2736277.2741093

Thé o Trouillon, Johannes Welbl, Sebastian Riedel, É ric Gaussier, and Guillaume Bouchard. 2016. Complex Embeddings for Simple Link Prediction. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19--24, 2016 (JMLR Workshop and Conference Proceedings, Vol. 48). JMLR.org, 2071--2080. http://proceedings.mlr.press/v48/trouillon16.html

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=rJXMpikCZ

Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, and Shivaram Venkataraman. 2023. MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. In Proceedings of the Eighteenth European Conference on Computer Systems, EuroSys 2023, Rome, Italy, May 8--12, 2023, Giuseppe Antonio Di Luna, Leonardo Querzoni, Alexandra Fedorova, and Dushyanth Narayanan (Eds.). ACM, 144--161. https://doi.org/10.1145/3552326.3567501

Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural Deep Network Embedding. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13--17, 2016. ACM, 1225--1234. https://doi.org/10.1145/2939672.2939753

Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018c. GraphGAN: Graph Representation Learning With Generative Adversarial Nets. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2--7, 2019, Sheila A. McIlraith and Kilian Q. Weinberger (Eds.). AAAI Press, 2508--2515. https://doi.org/10.1609/aaai.v32i1.11872

Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018b. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2019. ACM, 839--848. https://doi.org/10.1145/3219819.3219869

Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Shinjuku, Tokyo, Japan, August 7--11, 2017. ACM, 515--524. https://doi.org/10.1145/3077136.3080786

Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR, Vol. abs/1909.01315 (2019). showeprint[arXiv]1909.01315 http://arxiv.org/abs/1909.01315

Nan Wang, Lu Lin, Jundong Li, and Hongning Wang. 2022. Unbiased Graph Embedding with Biased Graph Observations. 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, 1423--1433. https://doi.org/10.1145/3485447.3512189

Xiang Wang, Yaokun Xu, Xiangnan He, Yixin Cao, Meng Wang, and Tat-Seng Chua. 2020. Reinforced Negative Sampling over Knowledge Graph for Recommendation. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020. ACM / IW3C2, 99--109. https://doi.org/10.1145/3366423.3380098

Zhouxia Wang, Tianshui Chen, Jimmy S. J. Ren, Weihao Yu, Hui Cheng, and Liang Lin. 2018a. Deep Reasoning with Knowledge Graph for Social Relationship Understanding. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13--19, 2018, Stockholm, Sweden. ijcai.org, 1021--1028. https://doi.org/10.24963/ijcai.2018/142

Da Xu, Chuanwei Ruan, Evren Kö rpeoglu, Sushant Kumar, and Kannan Achan. 2020. Product Knowledge Graph Embedding for E-commerce. In WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3--7, 2020. ACM, 672--680. https://doi.org/10.1145/3336191.3371778

Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. 2022. Negative Sampling for Contrastive Representation Learning: A Review. CoRR, Vol. abs/2206.00212 (2022). https://doi.org/10.48550/arXiv.2206.00212 showeprint[arXiv]2206.00212

Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings. http://arxiv.org/abs/1412.6575

Jianbang Yang, Dahai Tang, Xiaoniu Song, Lei Wang, Qiang Yin, Rong Chen, Wenyuan Yu, and Jingren Zhou. 2022a. GNNLab: a factored system for sample-based GNN training over GPUs. In EuroSys '22: Seventeenth European Conference on Computer Systems, Rennes, France, April 5 - 8, 2022. ACM, 417--434. https://doi.org/10.1145/3492321.3519557

Ruichao Yang, Xiting Wang, Yiqiao Jin, Chaozhuo Li, Jianxun Lian, and Xing Xie. 2022b. Reinforcement Subgraph Reasoning for Fake News Detection. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. ACM, 2253--2262. https://doi.org/10.1145/3534678.3539277

Zhen Yang, Ming Ding, Chang Zhou, Hongxia Yang, Jingren Zhou, and Jie Tang. 2020. Understanding Negative Sampling in Graph Representation Learning. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. ACM, 1666--1676. https://doi.org/10.1145/3394486.3403218

Zhen Yang, Ming Ding, Xu Zou, Jie Tang, Bin Xu, Chang Zhou, and Hongxia Yang. 2023. Region or Global? A Principle for Negative Sampling in Graph-Based Recommendation. IEEE Trans. Knowl. Data Eng., Vol. 35, 6 (2023), 6264--6277. https://doi.org/10.1109/TKDE.2022.3155155

Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2019. ACM, 974--983. https://doi.org/10.1145/3219819.3219890

Xiangxiang Zeng, Xinqi Tu, Yuansheng Liu, Xiangzheng Fu, and Yansen Su. 2022. Toward better drug discovery with knowledge graph. Current opinion in structural biology, Vol. 72 (2022), 114--126.

Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In The 36th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR '13, Dublin, Ireland - July 28 - August 01, 2013. ACM, 785--788. https://doi.org/10.1145/2484028.2484126

Yaoming Zhen and Junhui Wang. 2023. Community detection in general hypergraph via graph embedding. J. Amer. Statist. Assoc., Vol. 118, 543 (2023), 1620--1629.

Chenguang Zheng, Hongzhi Chen, Yuxuan Cheng, Zhezheng Song, Yifan Wu, Changji Li, James Cheng, Hao Yang, and Shuai Zhang. 2022. ByteGNN: Efficient Graph Neural Network Training at Large Scale. Proc. VLDB Endow., Vol. 15, 6 (2022), 1228--1242. https://www.vldb.org/pvldb/vol15/p1228-zheng.pdf

Da Zheng, Xiang Song, Chao Ma, Zeyuan Tan, Zihao Ye, Jin Dong, Hao Xiong, Zheng Zhang, and George Karypis. 2020. DGL-KE: Training Knowledge Graph Embeddings at Scale. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25--30, 2020. ACM, 739--748. https://doi.org/10.1145/3397271.3401172

Zhaocheng Zhu, Shizhen Xu, Jian Tang, and Meng Qu. 2019. GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13--17, 2019. ACM, 2494--2504. https://doi.org/10.1145/3308558.3313508