{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:13:45Z","timestamp":1780766025275,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"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":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512185","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:13:07Z","timestamp":1650863587000},"page":"1381-1391","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective"],"prefix":"10.1145","author":[{"given":"Lanning","family":"Wei","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Zhao","sequence":"additional","affiliation":[{"name":"4Paradigm. Inc, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, China and Lenovo, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML. PMLR, 21\u201329.","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver\u00a0Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML. PMLR, 21\u201329."},{"key":"e_1_3_2_1_2_1","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. ICLR."},{"key":"e_1_3_2_1_3_1","volume-title":"Rethinking Graph Neural Network Search from Message-passing. CVPR","author":"Cai Shaofei","year":"2021","unstructured":"Shaofei Cai, Liang Li, Jincan Deng, Beichen Zhang, Zheng-Jun Zha, Li Su, and Qingming Huang. 2021. Rethinking Graph Neural Network Search from Message-passing. CVPR (2021)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Deli Chen Yankai Lin Wei Li Peng Li Jie Zhou and Xu Sun. 2020. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In AAAI Vol.\u00a034. 3438\u20133445.","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Jiamin Chen Jianliang Gao Yibo Chen Moctard\u00a0Babatounde Oloulade Tengfei Lyu and Zhao Li. 2021. GraphPAS: Parallel Architecture Search for Graph Neural Networks. In SIGIR. 2182\u20132186.","DOI":"10.1145\/3404835.3463007"},{"key":"e_1_3_2_1_6_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR 1725\u20131735."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Xin Chen Lingxi Xie Jun Wu and Qi Tian. 2019. Progressive differentiable architecture search: Bridging the depth gap between search and evaluation. In ICCV. 1294\u20131303.","DOI":"10.1109\/ICCV.2019.00138"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-020-01396-x"},{"key":"e_1_3_2_1_9_1","unstructured":"Gabriele Corso Luca Cavalleri Dominique Beaini Pietro Li\u00f2 and Petar Veli\u010dkovi\u0107. 2020. Principal Neighbourhood Aggregation for Graph Nets. In NeurIPS Vol.\u00a033. 13260\u201313271."},{"key":"e_1_3_2_1_10_1","volume-title":"Adanet: Adaptive structural learning of artificial neural networks. In ICML. PMLR, 874\u2013883.","author":"Cortes Corinna","year":"2017","unstructured":"Corinna Cortes, Xavier Gonzalvo, Vitaly Kuznetsov, Mehryar Mohri, and Scott Yang. 2017. Adanet: Adaptive structural learning of artificial neural networks. In ICML. PMLR, 874\u2013883."},{"key":"e_1_3_2_1_11_1","volume-title":"Diffmg: Differentiable meta graph search for heterogeneous graph neural networks. In KDD. 279\u2013288.","author":"Ding Yuhui","year":"2021","unstructured":"Yuhui Ding, Quanming Yao, Huan Zhao, and Tong Zhang. 2021. Diffmg: Differentiable meta graph search for heterogeneous graph neural networks. In KDD. 279\u2013288."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Lun Du Xiaozhou Shi Qiang Fu Hengyu Liu Shi Han and Dongmei Zhang. 2022. GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily. In TheWebConf.","DOI":"10.1145\/3485447.3512201"},{"key":"e_1_3_2_1_13_1","unstructured":"Wenzheng Feng Jie Zhang Yuxiao Dong Yu Han Huanbo Luan Qian Xu Qiang Yang Evgeny Kharlamov and Jie Tang. 2020. Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NeurIPS 33(2020)."},{"key":"e_1_3_2_1_14_1","unstructured":"Matthias Fey and Jan\u00a0E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric."},{"key":"e_1_3_2_1_15_1","volume-title":"Graphnas: Graph neural architecture search with reinforcement learning. In IJCAI.","author":"Gao Yang","year":"2020","unstructured":"Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graphnas: Graph neural architecture search with reinforcement learning. In IJCAI."},{"key":"e_1_3_2_1_16_1","unstructured":"Justin Gilmer Samuel\u00a0S Schoenholz Patrick\u00a0F Riley Oriol Vinyals and George\u00a0E Dahl. 2017. Neural Message Passing for Quantum Chemistry. In ICML. 1263\u20131272."},{"key":"e_1_3_2_1_17_1","volume-title":"Dots: Decoupling operation and topology in differentiable architecture search. In CVPR. 12311\u201312320.","author":"Gu Yu-Chao","year":"2021","unstructured":"Yu-Chao Gu, Li-Juan Wang, Yun Liu, Yi Yang, Yu-Huan Wu, Shao-Ping Lu, and Ming-Ming Cheng. 2021. Dots: Decoupling operation and topology in differentiable architecture search. In CVPR. 12311\u201312320."},{"key":"e_1_3_2_1_18_1","volume-title":"Single path one-shot neural architecture search with uniform sampling","author":"Guo Zichao","unstructured":"Zichao Guo, Xiangyu Zhang, Haoyuan Mu, Wen Heng, Zechun Liu, Yichen Wei, and Jian Sun. 2020. Single path one-shot neural architecture search with uniform sampling. In ECCV. Springer, 544\u2013560."},{"key":"e_1_3_2_1_19_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024\u20131034."},{"key":"e_1_3_2_1_20_1","unstructured":"Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. In NeurIPS. 22118\u201322133."},{"key":"e_1_3_2_1_21_1","volume-title":"Laurens Van Der\u00a0Maaten, and Kilian\u00a0Q Weinberger","author":"Huang Gao","year":"2017","unstructured":"Gao Huang, Zhuang Liu, Laurens Van Der\u00a0Maaten, and Kilian\u00a0Q Weinberger. 2017. Densely connected convolutional networks. In CVPR. 4700\u20134708."},{"key":"e_1_3_2_1_22_1","volume-title":"InceptionGCN: receptive field aware graph convolutional network for disease prediction","author":"Kazi Anees","unstructured":"Anees Kazi, Shayan Shekarforoush, S\u00a0Arvind Krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kort\u00fcm, Seyed-Ahmad Ahmadi, Shadi Albarqouni, and Nassir Navab. 2019. InceptionGCN: receptive field aware graph convolutional network for disease prediction. In IPMI. Springer, 73\u201385."},{"key":"e_1_3_2_1_23_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. ICLR."},{"key":"e_1_3_2_1_24_1","unstructured":"Dawei Leng Jinjiang Guo Lurong Pan Jie Li and Xinyu Wang. 2021. Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations. arXiv preprint arXiv:2102.04064(2021)."},{"key":"e_1_3_2_1_25_1","volume-title":"Deepgcns: Can gcns go as deep as cnns?. In ICCV. 9267\u20139276.","author":"Li Guohao","year":"2019","unstructured":"Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. Deepgcns: Can gcns go as deep as cnns?. In ICCV. 9267\u20139276."},{"key":"e_1_3_2_1_26_1","volume-title":"Sgas: Sequential greedy architecture search. In CVPR. 1620\u20131630.","author":"Li Guohao","year":"2020","unstructured":"Guohao Li, Guocheng Qian, Itzel\u00a0C Delgadillo, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2020. Sgas: Sequential greedy architecture search. In CVPR. 1620\u20131630."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI Vol.\u00a032.","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Yaoman Li and Irwin King. 2020. AutoGraph: Automated Graph Neural Network. In ICONIP. 189\u2013201.","DOI":"10.1007\/978-3-030-63833-7_16"},{"key":"e_1_3_2_1_29_1","unstructured":"Yanxi Li Zean Wen Yunhe Wang and Chang Xu. 2021. One-shot Graph Neural Architecture Search with Dynamic Search Space. In AAAI."},{"key":"e_1_3_2_1_30_1","volume-title":"DARTS: Differentiable architecture search. ICLR.","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. DARTS: Differentiable architecture search. ICLR."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Meng Liu Hongyang Gao and Shuiwang Ji. 2020. Towards deeper graph neural networks. In KDD. 338\u2013348.","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Julian McAuley Christopher Targett Qinfeng Shi and Anton Van Den\u00a0Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR. 43\u201352.","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_33_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 8026\u20138037.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 8026\u20138037."},{"key":"e_1_3_2_1_34_1","volume-title":"Yu Lei, and Bo Yang.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. ICLR."},{"key":"e_1_3_2_1_35_1","unstructured":"Yijian Qin Xin Wang Zeyang Zhang and Wenwu Zhu. 2021. Graph Differentiable Architecture Search with Structure Learning. NeurIPS 34."},{"key":"e_1_3_2_1_36_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. ICLR.","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. Dropedge: Towards deep graph convolutional networks on node classification. ICLR."},{"key":"e_1_3_2_1_37_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93\u201393."},{"key":"e_1_3_2_1_38_1","unstructured":"Oleksandr Shchur Maximilian Mumme Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868(2018)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Diego Valsesia Giulia Fracastoro and Enrico Magli. 2020. Don\u2019t stack layers in graph neural networks wire them randomly.","DOI":"10.1002\/9781119850830.ch3"},{"key":"e_1_3_2_1_40_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. ICLR."},{"key":"e_1_3_2_1_41_1","unstructured":"Zhili Wang Shimin Di and Lei Chen. 2021. AutoGEL: An Automated Graph Neural Network with Explicit Link Information. NeurIPS 34(2021)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Zhenyi Wang Huan Zhao and Chuan Shi. 2022. Profiling the Design Space for Graph Neural Networks based Collaborative Filtering. In WSDM.","DOI":"10.1145\/3488560.3498520"},{"key":"e_1_3_2_1_43_1","unstructured":"Lanning Wei Huan Zhao Quanming Yao and Zhiqiang He. 2021. Pooling architecture search for graph classification. In CIKM. 2091\u20132100."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3473330"},{"key":"e_1_3_2_1_45_1","unstructured":"Sirui Xie Shoukang Hu Xinjiang Wang Chunxiao Liu Jianping Shi Xunying Liu and Dahua Lin. 2021. Understanding the wiring evolution in differentiable neural architecture search. In AISTATS. 874\u2013882."},{"key":"e_1_3_2_1_46_1","unstructured":"Saining Xie Alexander Kirillov Ross Girshick and Kaiming He. 2019. Exploring randomly wired neural networks for image recognition. In ICCV. 1284\u20131293."},{"key":"e_1_3_2_1_47_1","unstructured":"Sirui Xie Hehui Zheng Chunxiao Liu and Liang Lin. 2018. SNAS: stochastic neural architecture search. ICLR."},{"key":"e_1_3_2_1_48_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?ICLR."},{"key":"e_1_3_2_1_49_1","unstructured":"Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks. In ICML. 5453\u20135462."},{"key":"e_1_3_2_1_50_1","unstructured":"Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. NeurIPS 33."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Kun Yuan Quanquan Li Shaopeng Guo Dapeng Chen Aojun Zhou Fengwei Yu and Ziwei Liu. 2021. Differentiable Dynamic Wirings for Neural Networks. In ICCV. 327\u2013336.","DOI":"10.1109\/ICCV48922.2021.00038"},{"key":"e_1_3_2_1_52_1","volume-title":"Graphsaint: Graph sampling based inductive learning method. ICLR.","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2020. Graphsaint: Graph sampling based inductive learning method. ICLR."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Yongqi Zhang and Quanming Yao. 2022. Knowledge Graph Reasoning with Relational Directed Graph. In TheWebConf.","DOI":"10.1145\/3485447.3512008"},{"key":"e_1_3_2_1_54_1","volume-title":"Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. In NeurIPS.","author":"Zhang Yongqi","year":"2020","unstructured":"Yongqi Zhang, Quanming Yao, and Lei Chen. 2020. Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. In NeurIPS."},{"key":"e_1_3_2_1_55_1","volume-title":"AutoSF: Searching Scoring Functions for Knowledge Graph Embedding","author":"Zhang Yongqi","unstructured":"Yongqi Zhang, Quanming Yao, Wenyuan Dai, and Lei Chen. 2020. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. In ICDE. IEEE, 433\u2013444."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhang Xin Wang and Wenwu Zhu. 2021. Automated Machine Learning on Graphs: A Survey. In IJCAI. 4704\u20134712.","DOI":"10.24963\/ijcai.2021\/637"},{"key":"e_1_3_2_1_57_1","unstructured":"Huan Zhao Lanning Wei and Quanming Yao. 2020. Simplifying Architecture Search for Graph Neural Network."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"crossref","unstructured":"Huan Zhao Quanming Yao and Weiwei Tu. 2021. Search to aggregate neighborhood for graph neural network. In ICDE.","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"e_1_3_2_1_59_1","unstructured":"Jiong Zhu Yujun Yan Lingxiao Zhao Mark Heimann Leman Akoglu and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. In NeurIPS."},{"key":"e_1_3_2_1_60_1","unstructured":"Barret Zoph and Quoc\u00a0V Le. 2017. Neural architecture search with reinforcement learning. ICLR."}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512185","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512185","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:14Z","timestamp":1750188674000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":60,"alternative-id":["10.1145\/3485447.3512185","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512185","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}