{"type":"ScholarlyArticle","title":"Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention","authors":[{"name":"Hongjun Wang","slug":"hongjun-wang","given":"Hongjun","family":"Wang","affiliation":"Southern University of Science and Technology , Shenzhen , China"},{"name":"Jiyuan Chen","slug":"jiyuan-chen","given":"Jiyuan","family":"Chen","affiliation":"Southern University of Science and Technology , Shenzhen , China"},{"name":"Lun Du","slug":"lun-du","given":"Lun","family":"Du","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Qiang Fu","slug":"qiang-fu","given":"Qiang","family":"Fu","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Shi Han","slug":"shi-han","given":"Shi","family":"Han","affiliation":"Microsoft Research Asia , Beijing , China"},{"name":"Xuan Song","slug":"xuan-song","given":"Xuan","family":"Song","affiliation":"Southern University of Science and Technology , Shenzhen , China"}],"abstract":"Recent years have witnessed the great potential of attention mechanism in graph representation learning. However, while variants of attention-based GNNs are setting new benchmarks for numerous real-world datasets, recent works have pointed out that their induced attentions are less robust and generalizable against noisy graphs due to lack of direct supervision. In this paper, we present a new framework which utilizes the tool of causality to provide a powerful supervision signal for the learning process of attention functions. Specifically, we estimate the direct causal effect of attention to the final prediction, and then maximize such effect to guide attention attending to more meaningful neighbors. Our method can serve as a plug-and-play module for any canonical attention-based GNNs in an end-to-end fashion. Extensive experiments on a wide range of benchmark datasets illustrated that, by directly supervising attention functions, the model is able to converge faster with a clearer decision boundary, and thus yields better performances.","doi":"10.24963/ijcai.2023/257","url":"https://ijcai.org/proceedings/2023/257","pdf":"https://ijcai.org/proceedings/2023/0257.pdf","markdown":"https://ijcai.org/proceedings/2023/257.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/257","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":257,"pages":{"first":2315,"last":2323},"keywords":["Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Battaglia et al., 2016] Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al. 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