{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T05:07:40Z","timestamp":1737176860001,"version":"3.33.0"},"reference-count":57,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,15]]},"DOI":"10.1109\/bigdata62323.2024.10825838","type":"proceedings-article","created":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T18:31:23Z","timestamp":1737052283000},"page":"680-689","source":"Crossref","is-referenced-by-count":0,"title":["Automated Polynomial Filter Learning for Graph Neural Networks"],"prefix":"10.1109","author":[{"given":"Wendi","family":"Yu","sequence":"first","affiliation":[{"name":"Texas A&M University,Department of Computer Science and Engineering"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhichao","family":"Hou","sequence":"additional","affiliation":[{"name":"North Carolina State University,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaorui","family":"Liu","sequence":"additional","affiliation":[{"name":"North Carolina State University,Department of Computer Science"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Graph neural networks exponentially lose expressive power for node classification","year":"2019","author":"Oono","key":"ref1"},{"key":"ref2","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang","year":"2018","journal-title":"Advances in neural information processing systems"},{"article-title":"A fair comparison of graph neural networks for graph classification","year":"2019","author":"Errica","key":"ref3"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5455"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117921"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.2200\/S01045ED1V01Y202009AIM046"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1017\/9781108924184"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1606.09375"},{"article-title":"Semi-supervised classification with graph convolutional networks","year":"2016","author":"Kipf","key":"ref10"},{"article-title":"Adaptive universal generalized pagerank graph neural network","volume-title":"ICLR","author":"Chien","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2879624"},{"article-title":"Graph wavelet neural network","volume-title":"ICLR","author":"Xu","key":"ref13"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054830"},{"article-title":"Predict then propagate: Graph neural networks meet personalized pagerank","volume-title":"ICLR","author":"Klicpera","key":"ref15"},{"key":"ref16","first-page":"6861","article-title":"Simplifying graph convolutional networks","volume-title":"International conference on machine learning","author":"Wu"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"ref18","first-page":"14 239","article-title":"Bernnet: Learning arbitrary graph spectral filters via bernstein approximation","volume":"34","author":"He","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Simplifying graph convolutional networks","volume-title":"ICML","author":"Wu","key":"ref19"},{"key":"ref20","first-page":"22 118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Advances in neural information processing systems"},{"article-title":"Is homophily a necessity for graph neural networks?","volume-title":"International Conference on Learning Representations","author":"Ma","key":"ref21"},{"key":"ref22","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2013.104"},{"article-title":"Adaptive universal generalized pagerank graph neural network","year":"2020","author":"Chien","key":"ref25"},{"article-title":"Geom-gcn: Geometric graph convolutional networks","volume-title":"ICLR","author":"Pei","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/icpr48806.2021.9412278"},{"key":"ref28","first-page":"21","article-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing","volume-title":"international conference on machine learning","author":"Abu-El-Haija"},{"article-title":"Generalizing graph neural networks beyond homophily","year":"2020","author":"Zhu","key":"ref29"},{"key":"ref30","first-page":"1725","article-title":"Simple and deep graph convolutional networks","volume-title":"International conference on machine learning","author":"Chen"},{"article-title":"Adam: A method for stochastic optimization","year":"2014","author":"Kingma","key":"ref31"},{"key":"ref32","first-page":"40","article-title":"Revisiting semi-supervised learning with graph embeddings","volume-title":"ICML","author":"Yang"},{"article-title":"Darts: Differentiable architecture search","year":"2018","author":"Liu","key":"ref33"},{"article-title":"Fast graph representation learning with pytorch geometric","year":"2019","author":"Fey","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref36","first-page":"1","article-title":"Query-driven active surveying for collective classification","volume-title":"10th international workshop on mining and learning with graphs","volume":"8","author":"Namata"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5"},{"article-title":"Autoaugment: Learning augmentation policies from data","year":"2018","author":"Cubuk","key":"ref40"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"article-title":"Neural architecture search with reinforcement learning","year":"2016","author":"Zoph","key":"ref42"},{"key":"ref43","first-page":"1568","article-title":"Bilevel programming for hyperparameter optimization and meta-learning","volume-title":"International Conference on Machine Learning","author":"Franceschi"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"ref45","first-page":"1540","article-title":"Optimizing millions of hyperparameters by implicit differentiation","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics","volume":"108","author":"Lorraine"},{"key":"ref46","first-page":"17 009","article-title":"Design space for graph neural networks","volume":"33","author":"You","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Graphnas: Graph neural architecture search with reinforcement learning","year":"2019","author":"Gao","key":"ref47"},{"key":"ref48","doi-asserted-by":"crossref","DOI":"10.24963\/ijcai.2020\/195","article-title":"Graph neural architecture search","volume-title":"International joint conference on artificial intelligence","author":"Gao"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330848"},{"article-title":"Auto-gnn: Neural architecture search of graph neural networks","year":"2019","author":"Zhou","key":"ref50"},{"article-title":"Fgnas: Fpga-aware graph neural architecture search","year":"2020","author":"Lu","key":"ref51"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61377-8_21"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108752"},{"key":"ref54","first-page":"3864","article-title":"Autoattend: Automated attention representation search","volume-title":"International conference on machine learning","author":"Guan"},{"article-title":"Probabilistic dual network architecture search on graphs","year":"2020","author":"Zhao","key":"ref55"},{"article-title":"Efficient graph neural architecture search","year":"2021","author":"Zhao","key":"ref56"},{"article-title":"Learned low precision graph neural networks","year":"2020","author":"Zhao","key":"ref57"}],"event":{"name":"2024 IEEE International Conference on Big Data (BigData)","start":{"date-parts":[[2024,12,15]]},"location":"Washington, DC, USA","end":{"date-parts":[[2024,12,18]]}},"container-title":["2024 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10824975\/10824942\/10825838.pdf?arnumber=10825838","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T08:15:32Z","timestamp":1737101732000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10825838\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,15]]},"references-count":57,"URL":"https:\/\/doi.org\/10.1109\/bigdata62323.2024.10825838","relation":{},"subject":[],"published":{"date-parts":[[2024,12,15]]}}}