{"type":"ScholarlyArticle","title":"Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs","authors":[{"name":"Yinchuan Li","slug":"yinchuan-li","given":"Yinchuan","family":"Li","affiliation":"Huawei Noah's Ark Lab"},{"name":"Zhigang Li","slug":"zhigang-li","given":"Zhigang","family":"Li","affiliation":"Tianjin University"},{"name":"Wenqian Li","slug":"wenqian-li","given":"Wenqian","family":"Li","affiliation":"National University of Singapore"},{"name":"Yunfeng Shao","slug":"yunfeng-shao","given":"Yunfeng","family":"Shao","affiliation":"Huawei Noah's Ark Lab"},{"name":"Yan Zheng","slug":"yan-zheng","given":"Yan","family":"Zheng","affiliation":"Tianjin University"},{"name":"Jianye Hao","slug":"jianye-hao","given":"Jianye","family":"Hao","affiliation":"Huawei Noah's Ark Lab; Tianjin University"}],"abstract":"Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions. However, these algorithms struggle to learn policy distributions that are proportional to rewards and have limited exploration capabilities. In this paper, we innovatively formulate the graph active learning problem as a generative process, named GFlowGNN, which generates various samples through sequential actions with probabilities precisely proportional to a predefined reward function. Furthermore, we propose the concept of flow nodes and flow features to efficiently model graphs as flows based on generative flow networks, where the policy network is trained with specially designed rewards. Extensive experiments on real datasets show that the proposed approach has good exploration capability and transferability, outperforming various state-of-the-art methods.","doi":"10.24963/ijcai.2023/438","url":"https://ijcai.org/proceedings/2023/438","pdf":"https://ijcai.org/proceedings/2023/0438.pdf","markdown":"https://ijcai.org/proceedings/2023/438.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/438","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":"Machine Learning","track":"Main Track","number":438,"pages":{"first":3939,"last":3947},"keywords":["Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Active learning"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2021a] Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. 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