{"type":"ScholarlyArticle","title":"CensNet: Convolution with Edge-Node Switching in Graph Neural Networks","authors":[{"name":"Xiaodong Jiang","slug":"xiaodong-jiang","given":"Xiaodong","family":"Jiang","affiliation":"Department of Computer Science , University of Georgia , Athens , GA , USA; Department of Statistics , University of Georgia , Athens , GA , USA"},{"name":"Pengsheng Ji","slug":"pengsheng-ji","given":"Pengsheng","family":"Ji","affiliation":"Department of Statistics , University of Georgia , Athens , GA , USA"},{"name":"Sheng Li","slug":"sheng-li","given":"Sheng","family":"Li","affiliation":"Department of Computer Science , University of Georgia , Athens , GA , USA"}],"abstract":"In this paper, we present CensNet, Convolution with Edge-Node Switching graph neural network, for semi-supervised classification and regression in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using line graph of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach has achieved or matched the state-of-the-art performance.","doi":"10.24963/ijcai.2019/369","url":"https://ijcai.org/proceedings/2019/369","pdf":"https://ijcai.org/proceedings/2019/0369.pdf","markdown":"https://ijcai.org/proceedings/2019/369.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/369","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":369,"pages":{"first":2656,"last":2662},"keywords":["Machine Learning: Deep Learning","Machine Learning Applications: Other Applications","Machine Learning: Relational Learning","Machine Learning: Semi-Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Bronstein et al., 2017] Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 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