{"type":"ScholarlyArticle","title":"Initializing Then Refining: A Simple Graph Attribute Imputation Network","authors":[{"name":"Wenxuan Tu","slug":"wenxuan-tu","given":"Wenxuan","family":"Tu","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Sihang Zhou","slug":"sihang-zhou","given":"Sihang","family":"Zhou","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Xinwang Liu","slug":"xinwang-liu","given":"Xinwang","family":"Liu","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Yue Liu","slug":"yue-liu","given":"Yue","family":"Liu","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Zhiping Cai","slug":"zhiping-cai","given":"Zhiping","family":"Cai","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"En Zhu","slug":"en-zhu","given":"En","family":"Zhu","affiliation":"National University of Defense Technology , Changsha , China"},{"name":"Changwang Zhang","slug":"changwang-zhang","given":"Changwang","family":"Zhang","affiliation":"Tencent Technology , Shenzhen , China"},{"name":"Jieren Cheng","slug":"jieren-cheng","given":"Jieren","family":"Cheng","affiliation":"Hainan University , Haikou , China"}],"abstract":"Representation learning on the attribute-missing graphs, whose connection information is complete while the attribute information of some nodes is missing, is an important yet challenging task. To impute the missing attributes, existing methods isolate the learning processes of attribute and structure information embeddings, and force both resultant representations to align with a common in-discriminative normal distribution, leading to inaccurate imputation. To tackle these issues, we propose a novel graph-oriented imputation framework called initializing then refining (ITR), where we first employ the structure information for initial imputation, and then leverage observed attribute and structure information to adaptively refine the imputed latent variables. Specifically, we first adopt the structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by aggregating the reliable and informative embeddings of attribute-observed samples according to the affinity structure. Specially, in our refining process, the affinity structure is adaptively updated through iterations by calculating the sample-wise correlations upon the recomposed embeddings. Extensive experiments on four benchmark datasets verify the superiority of ITR against state-of-the-art methods.","doi":"10.24963/ijcai.2022/485","url":"https://ijcai.org/proceedings/2022/485","pdf":"https://ijcai.org/proceedings/2022/0485.pdf","markdown":"https://ijcai.org/proceedings/2022/485.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/485","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":485,"pages":{"first":3494,"last":3500},"keywords":["Machine Learning: Multi-view learning","Machine Learning: Representation learning","Machine Learning: Self-supervised Learning","Machine Learning: Autoencoders"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986467","length_sec":78,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984610","length_sec":332,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2019] Lei Chen, Shunwang Gong, Joan Bruna, and Michael M. 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