{"type":"ScholarlyArticle","title":"Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks","authors":[{"name":"Hogun Park","slug":"hogun-park","given":"Hogun","family":"Park","affiliation":"Department of Computer Science , Purdue University"},{"name":"Jennifer Neville","slug":"jennifer-neville","given":"Jennifer","family":"Neville","affiliation":"Department of Computer Science , Purdue University"}],"abstract":"Node classification is an important problem in relational machine learning. However, in scenarios where graph edges represent interactions among the entities (e.g., over time), the majority of current methods either summarize the interaction information into link weights or aggregate the links to produce a static graph. In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal-Static-Graph-Net (TSGNet). Our key insight is that leveraging both a static neighbor encoder, which can learn aggregate neighbor patterns, and a graph neural network-based recurrent unit, which can capture complex interaction patterns, improve the performance of node classification. In our experiments on node classification tasks, TSGNet produces significant gains compared to state-of-the-art methods—reducing classification error up to 24% and an average of 10% compared to the best competitor on four real-world networks and one synthetic dataset.","doi":"10.24963/ijcai.2019/447","url":"https://ijcai.org/proceedings/2019/447","pdf":"https://ijcai.org/proceedings/2019/0447.pdf","markdown":"https://ijcai.org/proceedings/2019/447.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/447","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 M-Z","track":"Main track","number":447,"pages":{"first":3223,"last":3230},"keywords":["Machine Learning Applications: Networks","Machine Learning: Relational Learning","Machine Learning: Classification","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2018] Jie Chen, Tengfei Ma, and Cao Xiao. 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