{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T18:50:17Z","timestamp":1768071017839,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1618339,1617729,1814322"],"award-info":[{"award-number":["1618339,1617729,1814322"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"US DoD DTRA","award":["tHDTRA1- 14-1-0040"],"award-info":[{"award-number":["tHDTRA1- 14-1-0040"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330956","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"1539-1548","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["Stability and Generalization of Graph Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Saurabh","family":"Verma","sequence":"first","affiliation":[{"name":"University of Minnesota Twin Cities, Minneapolis, MN, USA"}]},{"given":"Zhi-Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Minnesota Twin Cities, Minneapolis, MN, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/11503415_3"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1577069.1577085"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Rie K Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems. 25--32. Rie K Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems. 25--32.","DOI":"10.7551\/mitpress\/7503.003.0009"},{"key":"e_1_3_2_1_4_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993--2001. James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993--2001."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2004.1326716"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/76359.76371"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1162\/153244302760200704"},{"key":"e_1_3_2_1_8_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013"},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning. 955--963","author":"Cohen Nadav","year":"2016"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390179"},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Machine Learning. 2702--2711","author":"Dai Hanjun","year":"2016"},{"key":"e_1_3_2_1_12_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3837--3845. Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3837--3845."},{"key":"e_1_3_2_1_13_1","unstructured":"Olivier Delalleau and Yoshua Bengio. 2011. Shallow vs. deep sum-product networks. In Advances in Neural Information Processing Systems. 666--674. Olivier Delalleau and Yoshua Bengio. 2011. Shallow vs. deep sum-product networks. In Advances in Neural Information Processing Systems. 666--674."},{"key":"e_1_3_2_1_14_1","volume-title":"Quantum Walk Neural Networks for Graph-Structured Data. In International Workshop on Complex Networks and their Applications. Springer, 182--193","author":"Dernbach Stefan","year":"2018"},{"key":"e_1_3_2_1_15_1","unstructured":"David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems. 2224--2232. David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems. 2224--2232."},{"key":"e_1_3_2_1_16_1","volume-title":"Conference on Learning Theory . 907--940","author":"Eldan Ronen","year":"2016"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1046923"},{"key":"e_1_3_2_1_18_1","volume-title":"Learning Graph Representations with Embedding Propagation. arXiv preprint arXiv:1710.03059","author":"Garc'ia-Dur\u00e1n Alberto","year":"2017"},{"key":"e_1_3_2_1_19_1","volume-title":"Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212","author":"Gilmer Justin","year":"2017"},{"key":"e_1_3_2_1_20_1","volume-title":"Interlacing eigenvalues and graphs. Linear Algebra and its applications","author":"Haemers Willem H","year":"1995"},{"key":"e_1_3_2_1_21_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034."},{"key":"e_1_3_2_1_22_1","volume-title":"Train faster, generalize better: Stability of stochastic gradient descent. arXiv preprint arXiv:1509.01240","author":"Hardt Moritz","year":"2015"},{"key":"e_1_3_2_1_23_1","volume-title":"Probably approximately correct learning","author":"Haussler David"},{"key":"e_1_3_2_1_24_1","volume-title":"Leslie Pack Kaelbling, and Yoshua Bengio","author":"Kawaguchi Kenji","year":"2017"},{"key":"e_1_3_2_1_25_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016"},{"key":"e_1_3_2_1_26_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016"},{"key":"e_1_3_2_1_27_1","volume-title":"Horace Pan, Brandon Anderson, and Shubhendu Trivedi.","author":"Kondor Risi","year":"2018"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.laa.2007.06.029"},{"key":"e_1_3_2_1_29_1","volume-title":"Deriving neural architectures from sequence and graph kernels. arXiv preprint arXiv:1705.09037","author":"Lei Tao","year":"2017"},{"key":"e_1_3_2_1_30_1","volume-title":"Adaptive Graph Convolutional Neural Networks. arXiv preprint arXiv:1801.03226","author":"Li Ruoyu","year":"2018"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219530516400042"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10444-004-7634-z"},{"key":"e_1_3_2_1_33_1","unstructured":"Behnam Neyshabur Srinadh Bhojanapalli David McAllester and Nati Srebro. 2017. Exploring generalization in deep learning. In Advances in Neural Information Processing Systems. 5947--5956. Behnam Neyshabur Srinadh Bhojanapalli David McAllester and Nati Srebro. 2017. Exploring generalization in deep learning. In Advances in Neural Information Processing Systems. 5947--5956."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the 33rd annual international conference on machine learning. ACM .","author":"Niepert Mathias","year":"2016"},{"key":"e_1_3_2_1_35_1","volume-title":"Unifying local and non-local signal processing with graph CNNs. arXiv preprint arXiv:1702.07759","author":"Puy Gilles","year":"2017"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.556"},{"key":"e_1_3_2_1_37_1","volume-title":"European Semantic Web Conference . Springer, 593--607","author":"Schlichtkrull Michael"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2235192"},{"key":"e_1_3_2_1_39_1","volume-title":"Exploring graph-structured passage representation for multi-hop reading comprehension with graph neural networks. arXiv preprint arXiv:1809.02040","author":"Song Linfeng","year":"2018"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2017.2726981"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2012.08.070"},{"key":"e_1_3_2_1_42_1","volume-title":"Benefits of depth in neural networks. arXiv preprint arXiv:1602.04485","author":"Telgarsky Matus","year":"2016"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Antoine J-P Tixier Giannis Nikolentzos Polykarpos Meladianos and Michalis Vazirgiannis. 2018. Graph Classification with 2D Convolutional Neural Networks. (2018). Antoine J-P Tixier Giannis Nikolentzos Polykarpos Meladianos and Michalis Vazirgiannis. 2018. Graph Classification with 2D Convolutional Neural Networks. (2018).","DOI":"10.1007\/978-3-030-30493-5_54"},{"key":"e_1_3_2_1_44_1","volume-title":"Deep graph infomax. arXiv preprint arXiv:1809.10341","author":"Petar Velivc","year":"2018"},{"key":"e_1_3_2_1_45_1","volume-title":"Graph Capsule Convolutional Neural Networks. arXiv preprint arXiv:1805.08090","author":"Verma Saurabh","year":"2018"},{"key":"e_1_3_2_1_46_1","volume-title":"How Powerful are Graph Neural Networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018"},{"key":"e_1_3_2_1_47_1","volume-title":"Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530","author":"Zhang Chiyuan","year":"2016"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.11.002"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330956","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330956","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:04Z","timestamp":1750206364000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":48,"alternative-id":["10.1145\/3292500.3330956","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330956","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}