{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T05:37:13Z","timestamp":1780724233765,"version":"3.54.1"},"reference-count":150,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2022,11,29]]},"abstract":"<jats:p>Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data noise and data scarcity issues in deep graph learning, the research on graph data augmentation has intensified lately. However, conventional data augmentation methods can hardly handle graph-structured data which is defined in non-Euclidean space with multi-modality. In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems. Specifically, we first propose a taxonomy for graph data augmentation techniques and then provide a structured review by categorizing the related work based on the augmented information modalities. Moreover, we summarize the applications of graph data augmentation in two representative problems in data-centric deep graph learning: (1) reliable graph learning which focuses on enhancing the utility of input graph as well as the model capacity via graph data augmentation; and (2) low-resource graph learning which targets on enlarging the labeled training data scale through graph data augmentation. For each problem, we also provide a hierarchical problem taxonomy and review the existing literature related to graph data augmentation. Finally, we point out promising research directions and the challenges in future research.<\/jats:p>","DOI":"10.1145\/3575637.3575646","type":"journal-article","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T14:14:48Z","timestamp":1670508888000},"page":"61-77","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":206,"title":["Data Augmentation for Deep Graph Learning"],"prefix":"10.1145","volume":"24","author":[{"given":"Kaize","family":"Ding","sequence":"first","affiliation":[{"name":"Arizona State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanghang","family":"Tong","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[{"name":"Arizona State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"ICLR","author":"Alon U.","year":"2021","unstructured":"U. Alon and E. Yahav . On the bottleneck of graph neural networks and its practical implications . In ICLR , 2021 . U. Alon and E. Yahav. On the bottleneck of graph neural networks and its practical implications. In ICLR, 2021."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2006.44"},{"key":"e_1_2_1_3_1","volume-title":"Neural networks and their applications. Review of scientific instruments","author":"Bishop C. M.","year":"1994","unstructured":"C. M. Bishop . Neural networks and their applications. Review of scientific instruments , 1994 . C. M. Bishop. Neural networks and their applications. Review of scientific instruments, 1994."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_2_1_5_1","volume-title":"AAAI","author":"Bo D.","year":"2022","unstructured":"D. Bo , B. Hu , X. Wang , Z. Zhang , C. Shi , and J. Zhou . Regularizing graph neural networks via consistencydiversity graph augmentations . In AAAI , 2022 . D. Bo, B. Hu, X.Wang, Z. Zhang, C. Shi, and J. Zhou. Regularizing graph neural networks via consistencydiversity graph augmentations. In AAAI, 2022."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_2_1_7_1","volume-title":"TPAMI","author":"Bouritsas G.","year":"2022","unstructured":"G. Bouritsas , F. Frasca , S. P. Zafeiriou , and M. Bronstein . Improving graph neural network expressivity via subgraph isomorphism counting . TPAMI , 2022 . G. Bouritsas, F. Frasca, S. P. Zafeiriou, and M. Bronstein. Improving graph neural network expressivity via subgraph isomorphism counting. TPAMI, 2022."},{"key":"e_1_2_1_8_1","volume-title":"ICLR","author":"Cai C.","year":"2021","unstructured":"C. Cai , D. Wang , and Y. Wang . Graph coarsening with neural networks . In ICLR , 2021 . C. Cai, D.Wang, and Y.Wang. Graph coarsening with neural networks. In ICLR, 2021."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_2_1_10_1","volume-title":"Graph masked autoencoder. arXiv preprint arXiv:2202.08391","author":"Chen H.","year":"2022","unstructured":"H. Chen , S. Zhang , and G. Xu . Graph masked autoencoder. arXiv preprint arXiv:2202.08391 , 2022 . H. Chen, S. Zhang, and G. Xu. Graph masked autoencoder. arXiv preprint arXiv:2202.08391, 2022."},{"key":"e_1_2_1_11_1","volume-title":"ICLR","author":"Chen J.","year":"2018","unstructured":"J. Chen , T. Ma , and C. Xiao . Fastgcn: Fast learning with graph convolutional networks via importance sampling . In ICLR , 2018 . J. Chen, T. Ma, and C. Xiao. Fastgcn: Fast learning with graph convolutional networks via importance sampling. In ICLR, 2018."},{"key":"e_1_2_1_12_1","volume-title":"ICLR","author":"Chen L.","year":"2020","unstructured":"L. Chen , Z. Chen , and J. Bruna . On graph neural networks versus graph-augmented mlps . In ICLR , 2020 . L. Chen, Z. Chen, and J. Bruna. On graph neural networks versus graph-augmented mlps. In ICLR, 2020."},{"key":"e_1_2_1_13_1","volume-title":"NeurIPS","author":"Chen L.","year":"2020","unstructured":"L. Chen , Y. Yao , F. Xu , M. Xu , and H. Tong . Trading personalization for accuracy: Data debugging in collaborative filtering . NeurIPS , 2020 . L. Chen, Y. Yao, F. Xu, M. Xu, and H. Tong. Trading personalization for accuracy: Data debugging in collaborative filtering. NeurIPS, 2020."},{"key":"e_1_2_1_14_1","volume-title":"NeurIPS","author":"Chen M.","year":"2020","unstructured":"M. Chen , Z. Wei , B. Ding , Y. Li , Y. Yuan , X. Du , and J.-R. Wen . Scalable graph neural networks via bidirectional propagation . NeurIPS , 2020 . M. Chen, Z. Wei, B. Ding, Y. Li, Y. Yuan, X. Du, and J.-R. Wen. Scalable graph neural networks via bidirectional propagation. NeurIPS, 2020."},{"key":"e_1_2_1_15_1","volume-title":"TPAMI","author":"Chen X.","year":"2020","unstructured":"X. Chen , S. Chen , J. Yao , H. Zheng , Y. Zhang , and I. W. Tsang . Learning on attribute-missing graphs . TPAMI , 2020 . X. Chen, S. Chen, J. Yao, H. Zheng, Y. Zhang, and I. W. Tsang. Learning on attribute-missing graphs. TPAMI, 2020."},{"key":"e_1_2_1_16_1","volume-title":"NeurIPS","author":"Chen Y.","year":"2020","unstructured":"Y. Chen , L. Wu , and M. Zaki . Iterative deep graph learning for graph neural networks: Better and robust node embeddings . NeurIPS , 2020 . Y. Chen, L. Wu, and M. Zaki. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. NeurIPS, 2020."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467364"},{"key":"e_1_2_1_19_1","volume-title":"Batch virtual adversarial training for graph convolutional networks. arXiv preprint arXiv:1902.09192","author":"Deng Z.","year":"2019","unstructured":"Z. Deng , Y. Dong , and J. Zhu . Batch virtual adversarial training for graph convolutional networks. arXiv preprint arXiv:1902.09192 , 2019 . Z. Deng, Y. Dong, and J. Zhu. Batch virtual adversarial training for graph convolutional networks. arXiv preprint arXiv:1902.09192, 2019."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20605"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411922"},{"key":"e_1_2_1_24_1","volume-title":"Structural and semantic contrastive learning for self-supervised node representation learning. arXiv preprint arXiv:2202.08480","author":"Ding K.","year":"2022","unstructured":"K. Ding , Y. Wang , Y. Yang , and H. Liu . Structural and semantic contrastive learning for self-supervised node representation learning. arXiv preprint arXiv:2202.08480 , 2022 . K. Ding, Y. Wang, Y. Yang, and H. Liu. Structural and semantic contrastive learning for self-supervised node representation learning. arXiv preprint arXiv:2202.08480, 2022."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449927"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371789"},{"key":"e_1_2_1_27_1","volume-title":"TKDE","author":"Feng F.","year":"2019","unstructured":"F. Feng , X. He , J. Tang , and T.-S. Chua . Graph adversarial training: Dynamically regularizing based on graph structure . TKDE , 2019 . F. Feng, X. He, J. Tang, and T.-S. Chua. Graph adversarial training: Dynamically regularizing based on graph structure. TKDE, 2019."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512183"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512044"},{"key":"e_1_2_1_30_1","volume-title":"NeurIPS","author":"Feng W.","year":"2020","unstructured":"W. Feng , J. Zhang , Y. Dong , Y. Han , H. Luan , Q. Xu , Q. Yang , E. Kharlamov , and J. Tang . Graph random neural networks for semi-supervised learning on graphs . In NeurIPS , 2020 . W. Feng, J. Zhang, Y. Dong, Y. Han, H. Luan, Q. Xu, Q. Yang, E. Kharlamov, and J. Tang. Graph random neural networks for semi-supervised learning on graphs. In NeurIPS, 2020."},{"key":"e_1_2_1_31_1","volume-title":"ICML","author":"Franceschi L.","year":"2019","unstructured":"L. Franceschi , M. Niepert , M. Pontil , and X. He . Learning discrete structures for graph neural networks . In ICML , 2019 . L. Franceschi, M. Niepert, M. Pontil, and X. He. Learning discrete structures for graph neural networks. In ICML, 2019."},{"key":"e_1_2_1_32_1","volume-title":"Training robust graph neural networks with topology adaptive edge dropping. arXiv preprint arXiv:2106.02892","author":"Gao Z.","year":"2021","unstructured":"Z. Gao , S. Bhattacharya , L. Zhang , R. S. Blum , A. Ribeiro , and B. M. Sadler . Training robust graph neural networks with topology adaptive edge dropping. arXiv preprint arXiv:2106.02892 , 2021 . Z. Gao, S. Bhattacharya, L. Zhang, R. S. Blum, A. Ribeiro, and B. M. Sadler. Training robust graph neural networks with topology adaptive edge dropping. arXiv preprint arXiv:2106.02892, 2021."},{"key":"e_1_2_1_33_1","volume-title":"ICML","author":"Gilmer J.","year":"2017","unstructured":"J. Gilmer , S. S. Schoenholz , P. F. Riley , O. Vinyals , and G. E. Dahl . Neural message passing for quantum chemistry . In ICML , 2017 . J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural message passing for quantum chemistry. In ICML, 2017."},{"key":"e_1_2_1_34_1","volume-title":"ICLR","author":"Goodfellow I. J.","year":"2015","unstructured":"I. J. Goodfellow , J. Shlens , and C. Szegedy . Explaining and harnessing adversarial examples . In ICLR , 2015 . I. J. Goodfellow, J. Shlens, and C. Szegedy. Explaining and harnessing adversarial examples. In ICLR, 2015."},{"key":"e_1_2_1_35_1","volume-title":"ifmixup: Towards intrusion-free graph mixup for graph classification. arXiv preprint arXiv:2110.09344","author":"Guo H.","year":"2021","unstructured":"H. Guo and Y. Mao . ifmixup: Towards intrusion-free graph mixup for graph classification. arXiv preprint arXiv:2110.09344 , 2021 . H. Guo and Y. Mao. ifmixup: Towards intrusion-free graph mixup for graph classification. arXiv preprint arXiv:2110.09344, 2021."},{"key":"e_1_2_1_36_1","volume-title":"Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216","author":"Hamilton W. L.","year":"2017","unstructured":"W. L. Hamilton , R. Ying , and J. Leskovec . Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216 , 2017 . W. L. Hamilton, R. Ying, and J. Leskovec. Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216, 2017."},{"key":"e_1_2_1_37_1","volume-title":"ICML","author":"Han X.","year":"2022","unstructured":"X. Han , Z. Jiang , N. Liu , and X. Hu . G-mixup: Graph data augmentation for graph classification . In ICML , 2022 . X. Han, Z. Jiang, N. Liu, and X. Hu. G-mixup: Graph data augmentation for graph classification. In ICML, 2022."},{"key":"e_1_2_1_38_1","volume-title":"ICML","author":"Hassani K.","year":"2020","unstructured":"K. Hassani and A. H. Khasahmadi . Contrastive multiview representation learning on graphs . In ICML , 2020 . K. Hassani and A. H. Khasahmadi. Contrastive multiview representation learning on graphs. In ICML, 2020."},{"key":"e_1_2_1_39_1","volume-title":"Learning graph augmentations to learn graph representations. arXiv preprint arXiv:2201.09830","author":"Hassani K.","year":"2022","unstructured":"K. Hassani and A. H. Khasahmadi . Learning graph augmentations to learn graph representations. arXiv preprint arXiv:2201.09830 , 2022 . K. Hassani and A. H. Khasahmadi. Learning graph augmentations to learn graph representations. arXiv preprint arXiv:2201.09830, 2022."},{"key":"e_1_2_1_40_1","volume-title":"Scalable consistency training for graph neural networks via self-ensemble self-distillation. arXiv preprint arXiv:2110.06290","author":"Hawkins C.","year":"2021","unstructured":"C. Hawkins , V. N. Ioannidis , S. Adeshina , and G. Karypis . Scalable consistency training for graph neural networks via self-ensemble self-distillation. arXiv preprint arXiv:2110.06290 , 2021 . C. Hawkins, V. N. Ioannidis, S. Adeshina, and G. Karypis. Scalable consistency training for graph neural networks via self-ensemble self-distillation. arXiv preprint arXiv:2110.06290, 2021."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3149997"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939747"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"e_1_2_1_45_1","volume-title":"Pre-training graph neural networks for generic structural feature extraction. arXiv preprint arXiv:1905.13728","author":"Hu Z.","year":"2019","unstructured":"Z. Hu , C. Fan , T. Chen , K.-W. Chang , and Y. Sun . Pre-training graph neural networks for generic structural feature extraction. arXiv preprint arXiv:1905.13728 , 2019 . Z. Hu, C. Fan, T. Chen, K.-W. Chang, and Y. Sun. Pre-training graph neural networks for generic structural feature extraction. arXiv preprint arXiv:1905.13728, 2019."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5843"},{"key":"e_1_2_1_48_1","volume-title":"Graph warp module: an auxiliary module for boosting the power of graph neural networks in molecular graph analysis. arXiv preprint arXiv:1902.01020","author":"Ishiguro K.","year":"2019","unstructured":"K. Ishiguro , S.-i. Maeda , and M. Koyama . Graph warp module: an auxiliary module for boosting the power of graph neural networks in molecular graph analysis. arXiv preprint arXiv:1902.01020 , 2019 . K. Ishiguro, S.-i. Maeda, and M. Koyama. Graph warp module: an auxiliary module for boosting the power of graph neural networks in molecular graph analysis. arXiv preprint arXiv:1902.01020, 2019."},{"key":"e_1_2_1_49_1","volume-title":"WWW","author":"Jeh G.","year":"2003","unstructured":"G. Jeh and J. Widom . Scaling personalized web search . In WWW , 2003 . G. Jeh and J.Widom. Scaling personalized web search. In WWW, 2003."},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00031"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449914"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/204"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"e_1_2_1_55_1","volume-title":"ICLR","author":"Jin W.","year":"2022","unstructured":"W. Jin , L. Zhao , S. Zhang , Y. Liu , J. Tang , and N. Shah . Graph condensation for graph neural networks . In ICLR , 2022 . W. Jin, L. Zhao, S. Zhang, Y. Liu, J. Tang, and N. Shah. Graph condensation for graph neural networks. In ICLR, 2022."},{"key":"e_1_2_1_56_1","volume-title":"AISTATS","author":"Jin Y.","year":"2020","unstructured":"Y. Jin , A. Loukas , and J. JaJa . Graph coarsening with preserved spectral properties . In AISTATS , 2020 . Y. Jin, A. Loukas, and J. JaJa. Graph coarsening with preserved spectral properties. In AISTATS, 2020."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3170249"},{"key":"e_1_2_1_58_1","volume-title":"Variational graph autoencoders. arXiv preprint arXiv:1611.07308","author":"Kipf T. N.","year":"2016","unstructured":"T. N. Kipf and M. Welling . Variational graph autoencoders. arXiv preprint arXiv:1611.07308 , 2016 . T. N. Kipf and M. Welling. Variational graph autoencoders. arXiv preprint arXiv:1611.07308, 2016."},{"key":"e_1_2_1_59_1","volume-title":"ICLR","author":"Kipf T. N.","year":"2017","unstructured":"T. N. Kipf and M. Welling . Semi-supervised classification with graph convolutional networks . In ICLR , 2017 . T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017."},{"key":"e_1_2_1_60_1","volume-title":"ICLR","author":"Klicpera J.","year":"2018","unstructured":"J. Klicpera , A. Bojchevski , and S. G\u00a8unnemann . Predict then propagate: Graph neural networks meet personalized pagerank . In ICLR , 2018 . J. Klicpera, A. Bojchevski, and S. G\u00a8unnemann. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR, 2018."},{"key":"e_1_2_1_61_1","volume-title":"NeurIPS","author":"Klicpera J.","year":"2019","unstructured":"J. Klicpera , S. Wei\u00dfenberger , and S. G\u00a8unnemann . Diffusion improves graph learning . In NeurIPS , 2019 . J. Klicpera, S. Wei\u00dfenberger, and S. G\u00a8unnemann. Diffusion improves graph learning. In NeurIPS, 2019."},{"key":"e_1_2_1_62_1","volume-title":"ICML","author":"Kondor R. I.","year":"2002","unstructured":"R. I. Kondor and J. Lafferty . Diffusion kernels on graphs and other discrete structures . In ICML , 2002 . R. I. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete structures. In ICML, 2002."},{"key":"e_1_2_1_63_1","volume-title":"Flag: Adversarial data augmentation for graph neural networks. arXiv preprint arXiv:2010.09891","author":"Kong K.","year":"2020","unstructured":"K. Kong , G. Li , M. Ding , Z. Wu , C. Zhu , B. Ghanem , G. Taylor , and T. Goldstein . Flag: Adversarial data augmentation for graph neural networks. arXiv preprint arXiv:2010.09891 , 2020 . K. Kong, G. Li, M. Ding, Z. Wu, C. Zhu, B. Ghanem, G. Taylor, and T. Goldstein. Flag: Adversarial data augmentation for graph neural networks. arXiv preprint arXiv:2010.09891, 2020."},{"key":"e_1_2_1_64_1","volume-title":"AAAI","author":"Lee N.","year":"2022","unstructured":"N. Lee , J. Lee , and C. Park . Augmentation-free selfsupervised learning on graphs . In AAAI , 2022 . N. Lee, J. Lee, and C. Park. Augmentation-free selfsupervised learning on graphs. In AAAI, 2022."},{"key":"e_1_2_1_65_1","volume-title":"Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741","author":"Li J.","year":"2017","unstructured":"J. Li , D. Cai , and X. He . Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741 , 2017 . J. Li, D. Cai, and X. He. Learning graph-level representation for drug discovery. arXiv preprint arXiv:1709.03741, 2017."},{"key":"e_1_2_1_66_1","volume-title":"NeurIPS","author":"Li P.","year":"2020","unstructured":"P. Li , Y. Wang , H. Wang , and J. Leskovec . Distance encoding: Design provably more powerful neural networks for graph representation learning . NeurIPS , 2020 . P. Li, Y. Wang, H. Wang, and J. Leskovec. Distance encoding: Design provably more powerful neural networks for graph representation learning. NeurIPS, 2020."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5901"},{"key":"e_1_2_1_69_1","volume-title":"Graph neural network with curriculum learning for imbalanced node classification. arXiv preprint arXiv:2202.02529","author":"Li X.","year":"2022","unstructured":"X. Li , L. Wen , Y. Deng , F. Feng , X. Hu , L. Wang , and Z. Fan . Graph neural network with curriculum learning for imbalanced node classification. arXiv preprint arXiv:2202.02529 , 2022 . X. Li, L. Wen, Y. Deng, F. Feng, X. Hu, L. Wang, and Z. Fan. Graph neural network with curriculum learning for imbalanced node classification. arXiv preprint arXiv:2202.02529, 2022."},{"key":"e_1_2_1_70_1","volume-title":"ICML","author":"Liu S.","year":"2022","unstructured":"S. Liu , H. Dong , L. Li , T. Xu , Y. Rong , P. Zhao , J. Huang , and D. Wu . Local augmentation for graph neural networks . ICML , 2022 . S. Liu, H. Dong, L. Li, T. Xu, Y. Rong, P. Zhao, J. Huang, and D. Wu. Local augmentation for graph neural networks. ICML, 2022."},{"key":"e_1_2_1_71_1","volume-title":"NeurIPS","author":"Liu X.","year":"2021","unstructured":"X. Liu , J. Ding , W. Jin , H. Xu , Y. Ma , Z. Liu , and J. Tang . Graph neural networks with adaptive residual . NeurIPS , 2021 . X. Liu, J. Ding, W. Jin, H. Xu, Y. Ma, Z. Liu, and J. Tang. Graph neural networks with adaptive residual. NeurIPS, 2021."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3172903"},{"key":"e_1_2_1_73_1","volume-title":"Mathematics","author":"Liu Y.","year":"2022","unstructured":"Y. Liu , Z. Zhang , Y. Liu , and Y. Zhu . Gatsmote: Improving imbalanced node classification on graphs via attention and homophily . Mathematics , 2022 . Y. Liu, Z. Zhang, Y. Liu, and Y. Zhu. Gatsmote: Improving imbalanced node classification on graphs via attention and homophily. Mathematics, 2022."},{"key":"e_1_2_1_74_1","volume-title":"AISTATS","author":"Lucic A.","year":"2022","unstructured":"A. Lucic , M. A. Ter Hoeve , G. Tolomei , M. De Rijke , and F. Silvestri . Cf-gnnexplainer: Counterfactual explanations for graph neural networks . In AISTATS , 2022 . A. Lucic, M. A. Ter Hoeve, G. Tolomei, M. De Rijke, and F. Silvestri. Cf-gnnexplainer: Counterfactual explanations for graph neural networks. In AISTATS, 2022."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_2_1_76_1","volume-title":"Automated data augmentations for graph classification. arXiv preprint arXiv:2202.13248","author":"Luo Y.","year":"2022","unstructured":"Y. Luo , M. McThrow , W. Y. Au , T. Komikado , K. Uchino , K. Maruhash , and S. Ji . Automated data augmentations for graph classification. arXiv preprint arXiv:2202.13248 , 2022 . Y. Luo, M. McThrow, W. Y. Au, T. Komikado, K. Uchino, K. Maruhash, and S. Ji. Automated data augmentations for graph classification. arXiv preprint arXiv:2202.13248, 2022."},{"key":"e_1_2_1_77_1","volume-title":"Masked graph modeling for molecule generation. Nature communications","author":"Mahmood O.","year":"2021","unstructured":"O. Mahmood , E. Mansimov , R. Bonneau , and K. Cho . Masked graph modeling for molecule generation. Nature communications , 2021 . O. Mahmood, E. Mansimov, R. Bonneau, and K. Cho. Masked graph modeling for molecule generation. Nature communications, 2021."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20748"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_2_1_80_1","volume-title":"Stanford InfoLab","author":"Page L.","year":"1999","unstructured":"L. Page , S. Brin , R. Motwani , and T. Winograd . The pagerank citation ranking: Bringing order to the web. Technical report , Stanford InfoLab , 1999 . L. Page, S. Brin, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab, 1999."},{"key":"e_1_2_1_81_1","volume-title":"NeurIPS","author":"Park H.","year":"2021","unstructured":"H. Park , S. Lee , S. Kim , J. Park , J. Jeong , K.-M. Kim , J.-W. Ha , and H. J. Kim . Metropolis-hastings data augmentation for graph neural networks . NeurIPS , 2021 . H. Park, S. Lee, S. Kim, J. Park, J. Jeong, K.-M. Kim, J.-W. Ha, and H. J. Kim. Metropolis-hastings data augmentation for graph neural networks. NeurIPS, 2021."},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20767"},{"key":"e_1_2_1_83_1","volume-title":"Graph classification via deep learning with virtual nodes. arXiv preprint arXiv:1708.04357","author":"Pham T.","year":"2017","unstructured":"T. Pham , T. Tran , H. Dam , and S. Venkatesh . Graph classification via deep learning with virtual nodes. arXiv preprint arXiv:1708.04357 , 2017 . T. Pham, T. Tran, H. Dam, and S. Venkatesh. Graph classification via deep learning with virtual nodes. arXiv preprint arXiv:1708.04357, 2017."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_2_1_85_1","volume-title":"ICLR","author":"Rong Y.","year":"2019","unstructured":"Y. Rong , W. Huang , T. Xu , and J. Huang . Dropedge: Towards deep graph convolutional networks on node classification . In ICLR , 2019 . Y. Rong, W. Huang, T. Xu, and J. Huang. Dropedge: Towards deep graph convolutional networks on node classification. In ICLR, 2019."},{"key":"e_1_2_1_86_1","volume-title":"Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198","author":"Rossi E.","year":"2020","unstructured":"E. Rossi , F. Frasca , B. Chamberlain , D. Eynard , M. Bronstein , and F. Monti . Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 , 2020 . E. Rossi, F. Frasca, B. Chamberlain, D. Eynard, M. Bronstein, and F. Monti. Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198, 2020."},{"key":"e_1_2_1_87_1","volume-title":"On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features. arXiv preprint arXiv:2111.12128","author":"Rossi E.","year":"2021","unstructured":"E. Rossi , H. Kenlay , M. I. Gorinova , B. P. Chamberlain , X. Dong , and M. Bronstein . On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features. arXiv preprint arXiv:2111.12128 , 2021 . E. Rossi, H. Kenlay, M. I. Gorinova, B. P. Chamberlain, X. Dong, and M. Bronstein. On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features. arXiv preprint arXiv:2111.12128, 2021."},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.38"},{"key":"e_1_2_1_89_1","author":"Shorten C.","year":"2019","unstructured":"C. Shorten and T. M. Khoshgoftaar . A survey on image data augmentation for deep learning. Journal of Big Data , 2019 . C. Shorten and T. M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, 2019.","journal-title":"Journal of Big Data"},{"key":"e_1_2_1_90_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"e_1_2_1_91_1","volume-title":"Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528","author":"Sun L.","year":"2018","unstructured":"L. Sun , Y. Dou , C. Yang , J. Wang , P. S. Yu , L. He , and B. Li . Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528 , 2018 . L. Sun, Y. Dou, C. Yang, J.Wang, P. S. Yu, L. He, and B. Li. Adversarial attack and defense on graph data: A survey. arXiv preprint arXiv:1812.10528, 2018."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467373"},{"key":"e_1_2_1_93_1","volume-title":"NeurIPS","author":"Suresh S.","year":"2021","unstructured":"S. Suresh , P. Li , C. Hao , and J. Neville . Adversarial graph augmentation to improve graph contrastive learning . In NeurIPS , 2021 . S. Suresh, P. Li, C. Hao, and J. Neville. Adversarial graph augmentation to improve graph contrastive learning. In NeurIPS, 2021."},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.11.016"},{"key":"e_1_2_1_95_1","volume-title":"Mgae: Masked autoencoders for self-supervised learning on graphs. arXiv preprint arXiv:2201.02534","author":"Tan Q.","year":"2022","unstructured":"Q. Tan , N. Liu , X. Huang , R. Chen , S.-H. Choi , and X. Hu . Mgae: Masked autoencoders for self-supervised learning on graphs. arXiv preprint arXiv:2201.02534 , 2022 . Q. Tan, N. Liu, X. Huang, R. Chen, S.-H. Choi, and X. Hu. Mgae: Masked autoencoders for self-supervised learning on graphs. arXiv preprint arXiv:2201.02534, 2022."},{"key":"e_1_2_1_96_1","volume-title":"ICLR","author":"Thakoor S.","year":"2021","unstructured":"S. Thakoor , C. Tallec , M. G. Azar , M. Azabou , E. L. Dyer , R. Munos , P. Velickovi\u00b4c , and M. Valko . Largescale representation learning on graphs via bootstrapping . In ICLR , 2021 . S. Thakoor, C. Tallec, M. G. Azar, M. Azabou, E. L. Dyer, R. Munos, P. Velickovi\u00b4c, and M. Valko. Largescale representation learning on graphs via bootstrapping. In ICLR, 2021."},{"key":"e_1_2_1_97_1","volume-title":"The information bottleneck method. arXiv preprint physics\/0004057","author":"Tishby N.","year":"2000","unstructured":"N. Tishby , F. C. Pereira , and W. Bialek . The information bottleneck method. arXiv preprint physics\/0004057 , 2000 . N. Tishby, F. C. Pereira, and W. Bialek. The information bottleneck method. arXiv preprint physics\/0004057, 2000."},{"key":"e_1_2_1_98_1","volume-title":"ICLR","author":"Topping J.","year":"2022","unstructured":"J. Topping , F. Di Giovanni , B. P. Chamberlain , X. Dong , and M. M. Bronstein . Understanding oversquashing and bottlenecks on graphs via curvature . In ICLR , 2022 . J. Topping, F. Di Giovanni, B. P. Chamberlain, X. Dong, and M. M. Bronstein. Understanding oversquashing and bottlenecks on graphs via curvature. In ICLR, 2022."},{"key":"e_1_2_1_99_1","volume-title":"ICLR","author":"Velickovic P.","year":"2019","unstructured":"P. Velickovic , W. Fedus , W. L. Hamilton , P. Li'o , Y. Bengio , and R. D. Hjelm . Deep graph infomax . ICLR , 2019 . P. Velickovic, W. Fedus, W. L. Hamilton, P. Li'o, Y. Bengio, and R. D. Hjelm. Deep graph infomax. ICLR, 2019."},{"key":"e_1_2_1_100_1","volume-title":"ICML","author":"Verma V.","year":"2019","unstructured":"V. Verma , A. Lamb , C. Beckham , A. Najafi , I. Mitliagkas , D. Lopez-Paz , and Y. Bengio . Manifold mixup: Better representations by interpolating hidden states . In ICML , 2019 . V. Verma, A. Lamb, C. Beckham, A. Najafi, I. Mitliagkas, D. Lopez-Paz, and Y. Bengio. Manifold mixup: Better representations by interpolating hidden states. In ICML, 2019."},{"key":"e_1_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17203"},{"key":"e_1_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401133"},{"key":"e_1_2_1_103_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449952"},{"key":"e_1_2_1_104_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487075.3487162"},{"key":"e_1_2_1_105_1","volume-title":"Graphdefense: Towards robust graph convolutional networks. arXiv preprint arXiv:1911.04429","author":"Wang X.","year":"2019","unstructured":"X. Wang , X. Liu , and C.-J. Hsieh . Graphdefense: Towards robust graph convolutional networks. arXiv preprint arXiv:1911.04429 , 2019 . X. Wang, X. Liu, and C.-J. Hsieh. Graphdefense: Towards robust graph convolutional networks. arXiv preprint arXiv:1911.04429, 2019."},{"key":"e_1_2_1_106_1","volume-title":"Distance-wise prototypical graph neural network in node imbalance classification. arXiv preprint arXiv:2110.12035","author":"Wang Y.","year":"2021","unstructured":"Y. Wang , C. Aggarwal , and T. Derr . Distance-wise prototypical graph neural network in node imbalance classification. arXiv preprint arXiv:2110.12035 , 2021 . Y. Wang, C. Aggarwal, and T. Derr. Distance-wise prototypical graph neural network in node imbalance classification. arXiv preprint arXiv:2110.12035, 2021."},{"key":"e_1_2_1_107_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449796"},{"key":"e_1_2_1_108_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403063"},{"key":"e_1_2_1_109_1","doi-asserted-by":"publisher","DOI":"10.1109\/WCSP49889.2020.9299720"},{"key":"e_1_2_1_110_1","volume-title":"ICML","author":"Wu F.","year":"2019","unstructured":"F. Wu , A. Souza , T. Zhang , C. Fifty , T. Yu , and K. Weinberger . Simplifying graph convolutional networks . In ICML , 2019 . F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger. Simplifying graph convolutional networks. In ICML, 2019."},{"key":"e_1_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"e_1_2_1_112_1","volume-title":"et al. Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133","author":"Wu L.","year":"2021","unstructured":"L. Wu , H. Lin , Z. Gao , C. Tan , S. Li , et al. Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133 , 2021 . L. Wu, H. Lin, Z. Gao, C. Tan, S. Li, et al. Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context prediction. arXiv preprint arXiv:2106.11133, 2021."},{"key":"e_1_2_1_113_1","volume-title":"TKDE","author":"Wu L.","year":"2021","unstructured":"L. Wu , H. Lin , C. Tan , Z. Gao , and S. Z. Li . Selfsupervised learning on graphs: Contrastive, generative, or predictive . TKDE , 2021 . L. Wu, H. Lin, C. Tan, Z. Gao, and S. Z. Li. Selfsupervised learning on graphs: Contrastive, generative, or predictive. TKDE, 2021."},{"key":"e_1_2_1_114_1","volume-title":"NeurIPS","author":"Wu T.","year":"2020","unstructured":"T. Wu , H. Ren , P. Li , and J. Leskovec . Graph information bottleneck . In NeurIPS , 2020 . T. Wu, H. Ren, P. Li, and J. Leskovec. Graph information bottleneck. In NeurIPS, 2020."},{"key":"e_1_2_1_115_1","volume-title":"CIKM","author":"Wu X.","year":"2018","unstructured":"X. Wu , L. Zhao , and L. Akoglu . A quest for structure: jointly learning the graph structure and semisupervised classification . In CIKM , 2018 . X. Wu, L. Zhao, and L. Akoglu. A quest for structure: jointly learning the graph structure and semisupervised classification. In CIKM, 2018."},{"key":"e_1_2_1_116_1","volume-title":"TNNLS","author":"Wu Z.","year":"2020","unstructured":"Z. Wu , S. Pan , F. Chen , G. Long , C. Zhang , and S. Y. Philip . A comprehensive survey on graph neural networks . TNNLS , 2020 . Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip. A comprehensive survey on graph neural networks. TNNLS, 2020."},{"key":"e_1_2_1_117_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_2_1_118_1","volume-title":"NeurIPS","author":"Xie Q.","year":"2020","unstructured":"Q. Xie , Z. Dai , E. Hovy , T. Luong , and Q. Le . Unsupervised data augmentation for consistency training . In NeurIPS , 2020 . Q. Xie, Z. Dai, E. Hovy, T. Luong, and Q. Le. Unsupervised data augmentation for consistency training. In NeurIPS, 2020."},{"key":"e_1_2_1_119_1","volume-title":"TPAMI","author":"Xie Y.","year":"2022","unstructured":"Y. Xie , Z. Xu , J. Zhang , Z. Wang , and S. Ji . Selfsupervised learning of graph neural networks: A unified review . TPAMI , 2022 . Y. Xie, Z. Xu, J. Zhang, Z. Wang, and S. Ji. Selfsupervised learning of graph neural networks: A unified review. TPAMI, 2022."},{"key":"e_1_2_1_120_1","volume-title":"ICLR","author":"Xu K.","year":"2018","unstructured":"K. Xu , W. Hu , J. Leskovec , and S. Jegelka . How powerful are graph neural networks ? In ICLR , 2018 . K. Xu, W. Hu, J. Leskovec, and S. Jegelka. How powerful are graph neural networks? In ICLR, 2018."},{"key":"e_1_2_1_121_1","volume-title":"Graph sanitation with application to node classification. arXiv preprint arXiv:2105.09384","author":"Xu Z.","year":"2021","unstructured":"Z. Xu , B. Du , and H. Tong . Graph sanitation with application to node classification. arXiv preprint arXiv:2105.09384 , 2021 . Z. Xu, B. Du, and H. Tong. Graph sanitation with application to node classification. arXiv preprint arXiv:2105.09384, 2021."},{"key":"e_1_2_1_122_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/563"},{"key":"e_1_2_1_123_1","volume-title":"NeurIPS","author":"Yang L.","year":"2021","unstructured":"L. Yang , L. Zhang , and W. Yang . Graph adversarial self-supervised learning . In NeurIPS , 2021 . L. Yang, L. Zhang, and W. Yang. Graph adversarial self-supervised learning. In NeurIPS, 2021."},{"key":"e_1_2_1_124_1","doi-asserted-by":"publisher","DOI":"10.3115\/981658.981684"},{"key":"e_1_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3072345"},{"key":"e_1_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17283"},{"key":"e_1_2_1_127_1","volume-title":"ICML","author":"You Y.","year":"2021","unstructured":"Y. You , T. Chen , Y. Shen , and Z. Wang . Graph contrastive learning automated . In ICML , 2021 . Y. You, T. Chen, Y. Shen, and Z. Wang. Graph contrastive learning automated. In ICML, 2021."},{"key":"e_1_2_1_128_1","volume-title":"NeurIPS","author":"You Y.","year":"2020","unstructured":"Y. You , T. Chen , Y. Sui , T. Chen , Z. Wang , and Y. Shen . Graph contrastive learning with augmentations . In NeurIPS , 2020 . Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen. Graph contrastive learning with augmentations. In NeurIPS, 2020."},{"key":"e_1_2_1_129_1","volume-title":"ICML","author":"You Y.","year":"2020","unstructured":"Y. You , T. Chen , Z. Wang , and Y. Shen . When does self-supervision help graph convolutional networks ? In ICML , 2020 . Y. You, T. Chen, Z. Wang, and Y. Shen. When does self-supervision help graph convolutional networks? In ICML, 2020."},{"key":"e_1_2_1_130_1","volume-title":"NeurIPS","author":"Yue H.","year":"2022","unstructured":"H. Yue , C. Zhang , C. Zhang , and H. Liu . Labelinvariant augmentation for semi-supervised graph classification . In NeurIPS , 2022 . H. Yue, C. Zhang, C. Zhang, and H. Liu. Labelinvariant augmentation for semi-supervised graph classification. In NeurIPS, 2022."},{"key":"e_1_2_1_131_1","volume-title":"NeurIPS","author":"Zeng H.","year":"2021","unstructured":"H. Zeng , M. Zhang , Y. Xia , A. Srivastava , A. Malevich , R. Kannan , V. Prasanna , L. Jin , and R. Chen . Decoupling the depth and scope of graph neural networks . NeurIPS , 2021 . H. Zeng, M. Zhang, Y. Xia, A. Srivastava, A. Malevich, R. Kannan, V. Prasanna, L. Jin, and R. Chen. Decoupling the depth and scope of graph neural networks. NeurIPS, 2021."},{"key":"e_1_2_1_132_1","volume-title":"ICLR","author":"Zeng H.","year":"2019","unstructured":"H. Zeng , H. Zhou , A. Srivastava , R. Kannan , and V. Prasanna . Graphsaint: Graph sampling based inductive learning method . In ICLR , 2019 . H. Zeng, H. Zhou, A. Srivastava, R. Kannan, and V. Prasanna. Graphsaint: Graph sampling based inductive learning method. In ICLR, 2019."},{"key":"e_1_2_1_133_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17293"},{"key":"e_1_2_1_134_1","volume-title":"Defensevgae: Defending against adversarial attacks on graph data via a variational graph autoencoder. arXiv preprint arXiv:2006.08900","author":"Zhang A.","year":"2020","unstructured":"A. Zhang and J. Ma . Defensevgae: Defending against adversarial attacks on graph data via a variational graph autoencoder. arXiv preprint arXiv:2006.08900 , 2020 . A. Zhang and J. Ma. Defensevgae: Defending against adversarial attacks on graph data via a variational graph autoencoder. arXiv preprint arXiv:2006.08900, 2020."},{"key":"e_1_2_1_135_1","volume-title":"Improving the training of graph neural networks with consistency regularization. arXiv preprint arXiv:2112.04319","author":"Zhang C.","year":"2021","unstructured":"C. Zhang , Y. He , Y. Cen , Z. Hou , and J. Tang . Improving the training of graph neural networks with consistency regularization. arXiv preprint arXiv:2112.04319 , 2021 . C. Zhang, Y. He, Y. Cen, Z. Hou, and J. Tang. Improving the training of graph neural networks with consistency regularization. arXiv preprint arXiv:2112.04319, 2021."},{"key":"e_1_2_1_136_1","volume-title":"ICLR","author":"Zhang H.","year":"2018","unstructured":"H. Zhang , M. Cisse , Y. N. Dauphin , and D. Lopez- Paz . mixup: Beyond empirical risk minimization . In ICLR , 2018 . H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez- Paz. mixup: Beyond empirical risk minimization. In ICLR, 2018."},{"key":"e_1_2_1_137_1","volume-title":"Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140","author":"Zhang J.","year":"2020","unstructured":"J. Zhang , H. Zhang , C. Xia , and L. Sun . Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140 , 2020 . J. Zhang, H. Zhang, C. Xia, and L. Sun. Graph-bert: Only attention is needed for learning graph representations. arXiv preprint arXiv:2001.05140, 2020."},{"key":"e_1_2_1_138_1","volume-title":"NeurIPS","author":"Zhang M.","year":"2021","unstructured":"M. Zhang and P. Li . Nested graph neural networks . NeurIPS , 2021 . M. Zhang and P. Li. Nested graph neural networks. NeurIPS, 2021."},{"key":"e_1_2_1_139_1","volume-title":"Computational Social Networks","author":"Zhang S.","year":"2019","unstructured":"S. Zhang , H. Tong , J. Xu , and R. Maciejewski . Graph convolutional networks: a comprehensive review . Computational Social Networks , 2019 . S. Zhang, H. Tong, J. Xu, and R. Maciejewski. Graph convolutional networks: a comprehensive review. Computational Social Networks, 2019."},{"key":"e_1_2_1_140_1","volume-title":"NeurIPS","author":"Zhang X.","year":"2020","unstructured":"X. Zhang and M. Zitnik . Gnnguard: Defending graph neural networks against adversarial attacks . NeurIPS , 2020 . X. Zhang and M. Zitnik. Gnnguard: Defending graph neural networks against adversarial attacks. NeurIPS, 2020."},{"key":"e_1_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015829"},{"key":"e_1_2_1_142_1","volume-title":"NeurIPS","author":"Zhao J.","year":"2021","unstructured":"J. Zhao , Y. Dong , M. Ding , E. Kharlamov , and J. Tang . Adaptive diffusion in graph neural networks . In NeurIPS , 2021 . J. Zhao, Y. Dong, M. Ding, E. Kharlamov, and J. Tang. Adaptive diffusion in graph neural networks. In NeurIPS, 2021."},{"key":"e_1_2_1_143_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"e_1_2_1_144_1","volume-title":"ICML","author":"Zhao T.","year":"2022","unstructured":"T. Zhao , G. Liu , D. Wang , W. Yu , and M. Jiang . Learning from counterfactual links for link prediction . In ICML , 2022 . T. Zhao, G. Liu, D. Wang, W. Yu, and M. Jiang. Learning from counterfactual links for link prediction. In ICML, 2022."},{"key":"e_1_2_1_145_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17315"},{"key":"e_1_2_1_146_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_2_1_147_1","volume-title":"ICML","author":"Zheng C.","year":"2020","unstructured":"C. Zheng , B. Zong , W. Cheng , D. Song , J. Ni , W. Yu , H. Chen , and W. Wang . Robust graph representation learning via neural sparsification . In ICML , 2020 . C. Zheng, B. Zong, W. Cheng, D. Song, J. Ni, W. Yu, H. Chen, and W. Wang. Robust graph representation learning via neural sparsification. In ICML, 2020."},{"key":"e_1_2_1_148_1","volume-title":"Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131","author":"Zhu Y.","year":"2020","unstructured":"Y. Zhu , Y. Xu , F. Yu , Q. Liu , S. Wu , and L. Wang . Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 , 2020 . Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131, 2020."},{"key":"e_1_2_1_149_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_2_1_150_1","volume-title":"NeurIPS","author":"Zou D.","year":"2019","unstructured":"D. Zou , Z. Hu , Y. Wang , S. Jiang , Y. Sun , and Q. Gu . Layer-dependent importance sampling for training deep and large graph convolutional networks . NeurIPS , 2019 . D. Zou, Z. Hu, Y. Wang, S. Jiang, Y. Sun, and Q. Gu. Layer-dependent importance sampling for training deep and large graph convolutional networks. 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