{"type":"ScholarlyArticle","title":"Generalized Taxonomy-Guided Graph Neural Networks","authors":[{"name":"Yu Zhou","slug":"yu-zhou","given":"Yu","family":"Zhou","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Jianguo Wei","slug":"jianguo-wei","given":"Jianguo","family":"Wei","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Zhizhi Yu","slug":"zhizhi-yu","given":"Zhizhi","family":"Yu","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Weixiong Zhang","slug":"weixiong-zhang","given":"Weixiong","family":"Zhang","affiliation":"Department of Health Technology and Informatics , The Hong Kong Polytechnic University , Kowloon , Hong Kong"}],"abstract":"Graph neural networks have been demonstrated to be effective analytic apparatus for mining network data. Most real-world networks are inherently hierarchical, offering unique opportunities to acquire latent, intrinsic network organizational properties by utilizing network taxonomies. The existing approaches for learning implicit hierarchical network structures focus on introducing taxonomy to graph neural networks but often run short of exploiting the rich network semantics and structural properties in the taxonomy, resulting in poor generalizability and reusability. To address these issues, we propose generalized Taxonomy-Guided Graph Neural Networks (TG-GNN) to integrate taxonomy into network representation learning. We first construct a taxonomy representation learning module that introduces the concept of ego network to propagate and aggregate rich semantic and structural information in the taxonomy. We then design a taxonomy-guided Markov mechanism, which encapsulates taxonomy knowledge in pairwise potential functions, to refine network embeddings. Extensive experiments on various real-world networks illustrate the effectiveness of TG-GNN over the state-of-the-art methods on scenarios involving incomplete taxonomies and inductive settings.","doi":"10.24963/ijcai.2024/289","url":"https://ijcai.org/proceedings/2024/289","pdf":"https://ijcai.org/proceedings/2024/0289.pdf","markdown":"https://ijcai.org/proceedings/2024/289.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/289","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":289,"pages":{"first":2616,"last":2624},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Sequence and graph learning"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2021] Jiandong Bai, Jiawei Zhu, Yujiao Song, Ling Zhao, Zhixiang Hou, Ronghua Du, and Haifeng Li. 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