{"type":"ScholarlyArticle","title":"Multi-Channel Graph Neural Networks","authors":[{"name":"Kaixiong Zhou","slug":"kaixiong-zhou","given":"Kaixiong","family":"Zhou","affiliation":"Department of Computer Science and Engineering , Texas A&M University"},{"name":"Qingquan Song","slug":"qingquan-song","given":"Qingquan","family":"Song","affiliation":"Department of Computer Science and Engineering , Texas A&M University"},{"name":"Xiao Huang","slug":"xiao-huang","given":"Xiao","family":"Huang","affiliation":"Department of Computing , The Hong Kong Polytechnic University"},{"name":"Daochen Zha","slug":"daochen-zha","given":"Daochen","family":"Zha","affiliation":"Department of Computer Science and Engineering , Texas A&M University"},{"name":"Na Zou","slug":"na-zou","given":"Na","family":"Zou","affiliation":"Department of Industrial and Systems Engineering , Texas A&M University {zkxiong , song 3134 ,"},{"name":"Xia Hu","slug":"xia-hu","given":"Xia","family":"Hu","affiliation":"Department of Computer Science and Engineering , Texas A&M University"}],"abstract":"The classification of graph-structured data has be-come increasingly crucial in many disciplines.   It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification  applications.   A  straightforward  way  to leverage  the  hierarchical  structure  is  to  make  use the pooling algorithms to cluster nodes into fixed groups,  and shrink the input graph layer by layer to  learn  the  pooled  graphs.However,  the  pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the fixed clustering ignores the inherent multiple characteristics  of  nodes.    To  compensate  the  shrinking  loss  and  learn  the  various  nodes’  characteristics,  we  propose  the  multi-channel  graph  neural networks (MuchGNN). Motivated by the underlying  mechanisms  developed  in  convolutional  neural networks,  we define the tailored graph convolutions to learn a series of graph channels at each layer,  and  shrink  the  graphs  hierarchically  to  en-code the pooled structures. Experimental results on real-world datasets demonstrate the superiority of MuchGNN over the state-of-the-art methods.","doi":"10.24963/ijcai.2020/188","url":"https://ijcai.org/proceedings/2020/188","pdf":"https://ijcai.org/proceedings/2020/0188.pdf","markdown":"https://ijcai.org/proceedings/2020/188.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/188","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":188,"pages":{"first":1352,"last":1358},"keywords":["Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Applications","Data Mining: Classification, Semi-Supervised Learning"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24856/file_24856.m3u8","length_sec":273,"provider":"hls"}],"references":[{"position":1,"raw":"[Abu-El-Haija et al., 2018] Sami Abu-El-Haija, Amol Kapoor, Bryan Perozzi, and Joonseok Lee. 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