{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T09:32:22Z","timestamp":1750930342652,"version":"3.28.0"},"reference-count":36,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"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":[[2022,5,23]]},"DOI":"10.1109\/icassp43922.2022.9747447","type":"proceedings-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T19:50:34Z","timestamp":1651089034000},"page":"5563-5567","source":"Crossref","is-referenced-by-count":3,"title":["Dual Path Graph Convolutional Networks"],"prefix":"10.1109","author":[{"given":"Yunhe","family":"Li","sequence":"first","affiliation":[{"name":"University of Montreal Montreal,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaochen","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Montreal Montreal,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Montreal Montreal,Quebec,Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Graphnorm: A principled approach to accelerating graph neural network training","year":"2020","author":"cai","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482116"},{"key":"ref31","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"defferrard","year":"2016","journal-title":"Advances in neural information processing systems"},{"article-title":"Gated graph sequence neural networks","year":"2015","author":"li","key":"ref30"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"article-title":"On the bottleneck of graph neural networks and its practical implications","year":"2020","author":"alon","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"article-title":"Deepergcn: All you need to train deeper gcns","year":"2020","author":"li","key":"ref13"},{"article-title":"Simple and deep graph convolutional networks","year":"2020","author":"chen","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref16","first-page":"4467","article-title":"Dual path networks","author":"chen","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref17","first-page":"5453","article-title":"Representation learning on graphs with jumping knowledge networks","author":"xu","year":"2018","journal-title":"International Conference on Machine Learning"},{"key":"ref18","article-title":"How powerful are graph neural networks?","author":"xu","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref19","first-page":"4700","article-title":"Densely connected convolutional networks","author":"huang","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"},{"key":"ref27","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"hamilton","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref3","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"International Conference on Learning Representations"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref5","first-page":"2110","article-title":"Deepinf: Social influence prediction with deep learning","author":"qiu","year":"2018","journal-title":"ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00354"},{"key":"ref7","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"wang","year":"2019","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"ref2","article-title":"Neural message passing for quantum chemistry","author":"gilmer","year":"2017","journal-title":"ICML"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852455"},{"article-title":"Semi-supervised classification with graph convolutional networks","year":"2016","author":"kipf","key":"ref1"},{"article-title":"Open graph benchmark: Datasets for machine learning on graphs","year":"2020","author":"hu","key":"ref20"},{"article-title":"Fast graph representation learning with pytorch geometric","year":"2019","author":"fey","key":"ref22"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"article-title":"Improving graph neural network expressivity via subgraph isomorphism counting","year":"2020","author":"bouritsas","key":"ref24"},{"article-title":"Masked label prediction: Unified massage passing model for semi-supervised classification","year":"2020","author":"shi","key":"ref23"},{"key":"ref26","article-title":"A comprehensive survey on graph neural networks","author":"wu","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"article-title":"Graph neural networks: A review of methods and applications","year":"2018","author":"zhou","key":"ref25"}],"event":{"name":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","start":{"date-parts":[[2022,5,23]]},"location":"Singapore, Singapore","end":{"date-parts":[[2022,5,27]]}},"container-title":["ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9745891\/9746004\/09747447.pdf?arnumber=9747447","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T20:10:24Z","timestamp":1660594224000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9747447\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/icassp43922.2022.9747447","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]}}}