{"type":"ScholarlyArticle","title":"Survey on Graph Neural Network Acceleration: An Algorithmic Perspective","authors":[{"name":"Xin Liu","slug":"xin-liu","given":"Xin","family":"Liu","affiliation":"SKLCA , Institute of Computing Technology , Chinese Academy of Sciences , China; University of Chinese Academy of Sciences , China"},{"name":"Mingyu Yan","slug":"mingyu-yan","given":"Mingyu","family":"Yan","affiliation":"SKLCA , Institute of Computing Technology , Chinese Academy of Sciences , China"},{"name":"Lei Deng","slug":"lei-deng","given":"Lei","family":"Deng","affiliation":"Tsinghua University , China"},{"name":"Guoqi Li","slug":"guoqi-li","given":"Guoqi","family":"Li","affiliation":"University of Chinese Academy of Sciences , China; Institute of Automation , Chinese Academy of Sciences , China"},{"name":"Xiaochun Ye","slug":"xiaochun-ye","given":"Xiaochun","family":"Ye","affiliation":"SKLCA , Institute of Computing Technology , Chinese Academy of Sciences , China; University of Chinese Academy of Sciences , China"},{"name":"Dongrui Fan","slug":"dongrui-fan","given":"Dongrui","family":"Fan","affiliation":"SKLCA , Institute of Computing Technology , Chinese Academy of Sciences , China; University of Chinese Academy of Sciences , China"},{"name":"Shirui Pan","slug":"shirui-pan","given":"Shirui","family":"Pan","affiliation":"Monash University , Australia"},{"name":"Yuan Xie","slug":"yuan-xie","given":"Yuan","family":"Xie","affiliation":"University of California , Santa Barbara , America"}],"abstract":"Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research.","doi":"10.24963/ijcai.2022/772","url":"https://ijcai.org/proceedings/2022/772","pdf":"https://ijcai.org/proceedings/2022/0772.pdf","markdown":"https://ijcai.org/proceedings/2022/772.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/772","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":772,"pages":{"first":5521,"last":5529},"keywords":["Survey Track: Machine Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987286","length_sec":79,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985429","length_sec":589,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abadal et al., 2021] Sergi Abadal, Akshay Jain, Robert Guirado, and et al. Computing graph neural networks: A survey from algorithms to accelerators. ACM Computing Surveys (CSUR), 54(9):1-38, 2021.","title":"Computing graph neural networks: A survey from algorithms to accelerators","authors":"Akshay Sergi Abadal, Robert Jain, Guirado","year":"2021","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Arka et al., 2021] Aqeeb Iqbal Arka, Biresh Kumar Joardar, Janardhan Rao Doppa, and et al. Dare: Droplayer-aware manycore reram architecture for training graph neural net- works. In ICCAD, pages 1-9, 2021.","title":"Dare: Droplayer-aware manycore reram architecture for training graph neural networks","authors":"Arka","year":"2021","venue":"ICCAD","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bahri et al., 2021] Mehdi Bahri, Gaétan Bahl, and Stefanos Zafeiriou. Binary graph neural networks. In CVPR, pages 9492-9501, 2021.","title":"Binary graph neural networks","authors":"Mehdi Bahri, Gaétan Bahl, Stefanos Zafeiriou","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Battaglia et al., 2018] Peter W Battaglia, Jessica B Ham- rick, Victor Bapst, and et al. Relational inductive bi- ases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.","title":"Relational inductive biases, deep learning, and graph networks","authors":"Battaglia","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bojchevski et al., 2020] Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, and et al. Scaling graph neural networks with approximate pagerank. In SIGKDD, pages 2464-2473, 2020.","title":"Scaling graph neural networks with approximate pagerank","authors":"Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Boyd et al., 2011] Stephen Boyd, Neal Parikh, and Eric Chu. Distributed optimization and statistical learning via the alternating direction method of multipliers. Now Pub- lishers Inc, 2011.","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","authors":"Stephen Boyd, Neal Parikh, Eric Chu","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Buluc ¸et al., 2016] Aydın Buluc ¸, Henning Meyerhenke, Ilya Safro, and et al. Recent advances in graph partitioning. Algorithm engineering, pages 117-158, 2016.","title":"Recent advances in graph partitioning","authors":"Aydın Buluc, ¸, Henning Meyerhenke, Ilya Safro","year":"2016","venue":"Algorithm engineering","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chen et al., 2018a] Jianfei Chen, Jun Zhu, and Le Song. Stochastic training of graph convolutional networks with variance reduction. In ICML, pages 941-949, 2018.","title":"Stochastic training of graph convolutional networks with variance reduction","authors":"Chen","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2018b] Jie Chen, Tengfei Ma, and Cao Xiao. Fastgcn: Fast learning with graph convolutional networks via importance sampling. In ICLR, 2018.","title":"Fastgcn: Fast learning with graph convolutional networks via importance sampling","authors":"Chen","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Chen et al., 2021] Tianlong Chen, Yongduo Sui, Xuxi Chen, and et al. A unified lottery ticket hypothesis for graph neural networks. In ICML, pages 1695-1706, 2021.","title":"A unified lottery ticket hypothesis for graph neural networks","authors":"Tianlong Chen, Yongduo Sui, Xuxi Chen","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Cheng et al., 2017] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and ac- celeration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017.","title":"A survey of model compression and acceleration for deep neural networks","authors":"Yu Cheng, Duo Wang, Pan Zhou, Tao Zhang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Chiang et al., 2019] Wei-Lin Chiang, Xuanqing Liu, Si Si, and et al. Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks. In SIGKDD, pages 257-266, 2019.","title":"Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks","authors":"Wei-Lin Chiang, Xuanqing Liu, Si Si","year":"2019","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Eppstein et al., 1997] David Eppstein, Zvi Galil, Giuseppe F Italiano, and Amnon Nissenzweig. Spar- sification-a technique for speeding up dynamic graph algorithms. JACM, 44(5):669-696, 1997.","title":"Sparsification-a technique for speeding up dynamic graph algorithms","authors":"David Eppstein, Zvi Galil, Giuseppe F Italiano, Amnon Nissenzweig","year":"1997","venue":"JACM","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Fey and Lenssen, 2019] Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geomet- ric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.","title":"Fast graph representation learning with PyTorch Geometric","authors":"Matthias Fey, Jan E Lenssen","year":"2019","venue":"ICLR Workshop on Representation Learning on Graphs and Manifolds","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Hamilton et al., 2017] William L Hamilton, Rex Ying, and Jure Leskovec. Inductive representation learning on large graphs. In NeurIPS, pages 1025-1035, 2017.","title":"Inductive representation learning on large graphs","authors":"Rex William L Hamilton, Jure Ying, Leskovec","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Han et al., 2021] Li Han, Yan Mingyu, Lü Zhengyang, Li Wenming, Ye Xiaochun, Fan Dongrui, and Tang Zhimin. Survey on graph neural network acceleration ar- chitectures. Journal of Computer Research and Develop- ment, 58(6):1204, 2021.","title":"Survey on graph neural network acceleration architectures","authors":"Li Han, Yan Mingyu, Lü Zhengyang, Li Wenming, Ye Xiaochun, Fan Dongrui, Tang Zhimin","year":"2021","venue":"Journal of Computer Research and Development","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[He et al., 2020] Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR, pages 639-648, 2020.","title":"Lightgcn: Simplifying and powering graph convolution network for recommendation","authors":"He","year":"2020","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hinton et al., 2015] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.","title":"Distilling the knowledge in a neural network","authors":"Geoffrey Hinton, Oriol Vinyals, Jeff Dean","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hu et al., 2020a] Weihua Hu, Matthias Fey, Marinka Zit- nik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687, 2020.","title":"Open graph benchmark: Datasets for machine learning on graphs","authors":"Hu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hu et al., 2020b] Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. Heterogeneous graph transformer. In WWW, pages 2704-2710, 2020.","title":"Heterogeneous graph transformer","authors":"Hu","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Huang et al., 2018] Wenbing Huang, Tong Zhang, Yu Rong, and et al. Adaptive sampling towards fast graph representation learning. Advances in Neural Information Processing Systems, 31:4558-4567, 2018.","title":"Adaptive sampling towards fast graph representation learning","authors":"Huang","year":"2018","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Karypis and Kumar, 1998] George Karypis and Vipin Ku- mar. A fast and high quality multilevel scheme for parti- tioning irregular graphs. SIAM Journal on scientific Com- puting, 20(1):359-392, 1998.","title":"George Karypis and Vipin Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs","authors":"Kumar Karypis","year":"1998","venue":"SIAM Journal on scientific Computing","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Lamb et al., 2020] Luís C. Lamb, Artur S. d'Avila Garcez, Marco Gori, Marcelo O. R. Prates, Pedro H. C. Avelar, and Moshe Y. Vardi. Graph neural networks meet neural- symbolic computing: A survey and perspective. In IJCAI, pages 4877-4884, 2020.","title":"Graph neural networks meet neuralsymbolic computing: A survey and perspective","authors":"C Luís, Artur S Lamb, Marco Garcez, Marcelo O R Gori, Prates, H C Pedro, Moshe Y Avelar, Vardi","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Li et al., 2020] Jiayu Li, Tianyun Zhang, Hao Tian, Sheng- min Jin, Makan Fardad, and Reza Zafarani. Sgcn: A graph sparsifier based on graph convolutional networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 275-287. Springer, 2020.","title":"Sgcn: A graph sparsifier based on graph convolutional networks","authors":"Jiayu Li, Tianyun Zhang, Shengmin Hao Tian, Makan Jin, Reza Fardad, Zafarani","year":"2020","venue":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Li et al., 2021] Dongyue Li, Tao Yang, Lun Du, and et al. Adaptivegcn: Efficient gcn through adaptively sparsifying graphs. In CIKM, pages 3206-3210, 2021.","title":"Adaptivegcn: Efficient gcn through adaptively sparsifying graphs","authors":"Dongyue Li, Tao Yang, Lun Du","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Lin et al., 2020] Zhiqi Lin, Cheng Li, Youshan Miao, and et al. Pagraph: Scaling gnn training on large graphs via computation-aware caching. In SoCC, pages 401-415, 2020.","title":"Pagraph: Scaling gnn training on large graphs via computation-aware caching","authors":"Lin","year":"2020","venue":"SoCC","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2021a] Tianfeng Liu, Yangrui Chen, Dan Li, and et al. Bgl: Gpu-efficient gnn training by optimiz- ing graph data i/o and preprocessing. arXiv preprint arXiv:2112.08541, 2021.","title":"Bgl: Gpu-efficient gnn training by optimizing graph data i/o and preprocessing","authors":"Liu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Liu et al., 2021b] Xin Liu, Mingyu Yan, Lei Deng, and et al. Sampling methods for efficient training of graph convolu- tional networks: A survey. IEEE/CAA Journal of Auto- matica Sinica, 9(2):205-234, 2021.","title":"Sampling methods for efficient training of graph convolutional networks: A survey","authors":"Liu","year":"2021","venue":"IEEE/CAA Journal of Automatica Sinica","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Liu et al., 2021c] Xin Liu, Mingyu Yan, Shuhan Song, and et al. Gnnsampler: Bridging the gap between sam- pling algorithms of gnn and hardware. arXiv preprint arXiv:2108.11571, 2021.","title":"Gnnsampler: Bridging the gap between sampling algorithms of gnn and hardware","authors":"Liu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Mao et al., 2021] Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, and Xiuqiang He. Ultragcn: Ultra simplification of graph convolutional networks for recom- mendation. In CIKM, pages 1253-1262, 2021.","title":"Ultragcn: Ultra simplification of graph convolutional networks for recommendation","authors":"Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Md et al., 2021] Vasimuddin Md, Sanchit Misra, Guixiang Ma, and et al. Distgnn: Scalable distributed training for large-scale graph neural networks. arXiv preprint arXiv:2104.06700, 2021.","title":"Distgnn: Scalable distributed training for large-scale graph neural networks","authors":"Md","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Pope et al., 2019] Phillip E Pope, Soheil Kolouri, Moham- mad Rostami, Charles E Martin, and Heiko Hoffmann. Ex- plainability methods for graph convolutional neural net- works. In CVPR, pages 10772-10781, 2019.","title":"Explainability methods for graph convolutional neural networks","authors":"Soheil Phillip E Pope, Mohammad Kolouri, Charles E Rostami, Heiko Martin, Hoffmann","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Rong et al., 2020] Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. Dropedge: Towards deep graph con- volutional networks on node classification. In ICLR, 2020.","title":"Dropedge: Towards deep graph convolutional networks on node classification","authors":"Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Scarselli et al., 2008] Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. The graph neural network model. IEEE transactions on neural networks, 20(1):61-80, 2008.","title":"The graph neural network model","authors":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, Gabriele Monfardini","year":"2008","venue":"IEEE transactions on neural networks","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Srinivasa et al., 2020] Rakshith S Srinivasa, Cao Xiao, Lu- cas Glass, and et al. Fast graph attention networks us- ing effective resistance based graph sparsification. arXiv preprint arXiv:2006.08796, 2020.","title":"Fast graph attention networks using effective resistance based graph sparsification","authors":"Cao Rakshith S Srinivasa, Lucas Xiao, Glass","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Tailor et al., 2021] Shyam Anil Tailor, Javier Fernández- Marqués, and Nicholas Donald Lane. Degree-quant: Quantization-aware training for graph neural networks. In ICLR, 2021.","title":"Degree-quant: Quantization-aware training for graph neural networks","authors":"Anil Shyam, Javier Tailor, Nicholas Fernández-Marqués, Lane Donald","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Veličković et al., 2018] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph Attention Networks. ICLR, 2018.","title":"Graph Attention Networks","authors":"Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2019a] Minjie Wang, Da Zheng, Zihao Ye, and et al. Deep graph library: A graph-centric, highly- performant package for graph neural networks. arXiv preprint arXiv:1909.01315, 2019.","title":"Deep graph library: A graph-centric, highlyperformant package for graph neural networks","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wang et al., 2019b] Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. Neural graph col- laborative filtering. In SIGIR, pages 165-174, 2019.","title":"Neural graph collaborative filtering","authors":"Wang","year":"2019","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Wang et al., 2021a] Junfu Wang, Yunhong Wang, Zhen Yang, and et al. Bi-gcn: Binary graph convolutional net- work. In CVPR, pages 1561-1570, 2021.","title":"Bi-gcn: Binary graph convolutional network","authors":"Wang","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Wang et al., 2021b] Shoujin Wang, Liang Hu, Yan Wang, and et al. Graph learning based recommender systems: A review. In IJCAI, pages 4644-4652, 2021.","title":"Graph learning based recommender systems: A review","authors":"Wang","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/630"},{"position":43,"raw":"[Wu et al., 2019] Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. Sim- plifying graph convolutional networks. In ICML, pages 6861-6871, 2019.","title":"Simplifying graph convolutional networks","authors":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, Kilian Weinberger","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Wu et al., 2020] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE TNNLS, 32(1):4-24, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural net- works? In ICLR, 2019.","title":"How powerful are graph neural networks?","authors":"Xu","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Yan et al., 2019] Mingyu Yan, Xing Hu, Shuangchen Li, Abanti Basak, Han Li, Xin Ma, Itir Akgun, Yujing Feng, Peng Gu, Lei Deng, et al. Alleviating irregularity in graph analytics acceleration: A hardware/software co-design ap- proach. In Proceedings of the 52nd Annual IEEE/ACM In- ternational Symposium on Microarchitecture, pages 615- 628, 2019.","title":"Alleviating irregularity in graph analytics acceleration: A hardware/software co-design approach","authors":"Mingyu Yan, Xing Hu, Shuangchen Li, Abanti Basak, Han Li, Xin Ma, Itir Akgun, Yujing Feng, Peng Gu, Lei Deng","year":"2019","venue":"Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Yan et al., 2020a] Bencheng Yan, Chaokun Wang, Gaoyang Guo, and Yunkai Lou. Tinygnn: Learning efficient graph neural networks. In SIGKDD, pages 1848-1856, 2020.","title":"Tinygnn: Learning efficient graph neural networks","authors":"Yan","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Yan et al., 2020b] Mingyu Yan, Zhaodong Chen, Lei Deng, Xiaochun Ye, Zhimin Zhang, Dongrui Fan, and Yuan Xie. Characterizing and understanding gcns on gpu. IEEE Computer Architecture Letters, 19(1):22-25, 2020.","title":"Characterizing and understanding gcns on gpu","authors":"Yan","year":"2020","venue":"IEEE Computer Architecture Letters","doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Yan et al., 2020c] Mingyu Yan, Lei Deng, Xing Hu, and et al. Hygcn: A gcn accelerator with hybrid architec- ture. In 2020 IEEE International Symposium on High Per- formance Computer Architecture (HPCA), pages 15-29. IEEE, 2020.","title":"Hygcn: A gcn accelerator with hybrid architecture","authors":"Yan","year":"2020","venue":"IEEE International Symposium on High Performance Computer Architecture (HPCA)","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Yang et al., 2020] Yiding Yang, Jiayan Qiu, Mingli Song, Dacheng Tao, and Xinchao Wang. Distilling knowledge from graph convolutional networks. In CVPR, pages 7074-7083, 2020.","title":"Distilling knowledge from graph convolutional networks","authors":"Yang","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Yang et al., 2021] Cheng Yang, Jiawei Liu, and Chuan Shi. Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework. In WWW, pages 1227-1237, 2021.","title":"Extract the knowledge of graph neural networks and go beyond it: An effective knowledge distillation framework","authors":"Cheng Yang, Jiawei Liu, Chuan Shi","year":"2021","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Ying et al., 2019] Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. Gnnexplainer: Gener- ating explanations for graph neural networks. Advances in neural information processing systems, 32:9240, 2019.","title":"Gnnexplainer: Generating explanations for graph neural networks","authors":"Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Zeng et al., 2019] Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Accu- rate, efficient and scalable graph embedding. In IPDPS, pages 462-471. IEEE, 2019.","title":"Accurate, efficient and scalable graph embedding","authors":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna","year":"2019","venue":"IPDPS","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Zeng et al., 2020] Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Graph- SAINT: Graph sampling based inductive learning method. In ICLR, 2020.","title":"Graph-SAINT: Graph sampling based inductive learning method","authors":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhang et al., 2019] Chuxu Zhang, Dongjin Song, Chao Huang, and et al. Heterogeneous graph neural network. In SIGKDD, pages 793-803, 2019.","title":"Heterogeneous graph neural network","authors":"Zhang","year":"2019","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhang et al., 2020] Ziwei Zhang, Peng Cui, and Wenwu Zhu. Deep learning on graphs: A survey. IEEE TKDE, 2020.","title":"Deep learning on graphs: A survey","authors":"Ziwei Zhang, Peng Cui, Wenwu Zhu","year":"2020","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Zhang et al., 2021] Ziwei Zhang, Xin Wang, and Wenwu Zhu. Automated machine learning on graphs: A survey. In IJCAI, pages 4704-4712, 2021.","title":"Automated machine learning on graphs: A survey","authors":"Ziwei Zhang, Xin Wang, Wenwu Zhu","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/637"},{"position":58,"raw":"[Zhao et al., 2021] Tong Zhao, Yozen Liu, Leonardo Neves, and et al. Data augmentation for graph neural networks. In AAAI, pages 11015-11023, 2021.","title":"Data augmentation for graph neural networks","authors":"Tong Zhao, Yozen Liu, Leonardo Neves","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Zheng et al., 2020a] Cheng Zheng, Bo Zong, Wei Cheng, and et al. Robust graph representation learning via neu- ral sparsification. In ICML, pages 11458-11468, 2020.","title":"Robust graph representation learning via neural sparsification","authors":"Zheng","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Zheng et al., 2020b] Da Zheng, Chao Ma, Minjie Wang, and et al. Distdgl: distributed graph neural network train- ing for billion-scale graphs. In 2020 IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3), pages 36-44, 2020.","title":"Distdgl: distributed graph neural network training for billion-scale graphs","authors":"Zheng","year":"2020","venue":"IEEE/ACM 10th Workshop on Irregular Applications: Architectures and Algorithms (IA3)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}