{"type":"ScholarlyArticle","title":"Graph Neural Networks for Brain Graph Learning: A Survey","authors":[{"name":"Xuexiong Luo","slug":"xuexiong-luo","given":"Xuexiong","family":"Luo","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Jian Yang","slug":"jian-yang","given":"Jian","family":"Yang","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Shan Xue","slug":"shan-xue","given":"Shan","family":"Xue","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Amin Beheshti","slug":"amin-beheshti","given":"Amin","family":"Beheshti","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"Quan Z. Sheng","slug":"quan-z-sheng","given":"Quan Z.","family":"Sheng","affiliation":"School of Computing , Macquarie University , Sydney , Australia"},{"name":"David McAlpine","slug":"david-mcalpine","given":"David","family":"McAlpine","affiliation":"Department of Linguistics , Macquarie University , Sydney , Australia"},{"name":"Paul Sowman","slug":"paul-sowman","given":"Paul","family":"Sowman","affiliation":"School of Psychological Sciences , Macquarie University , Sydney , Australia; School of Clinical Sciences , Auckland University of Technology , Auckland , New Zealand"},{"name":"Alexis Giral","slug":"alexis-giral","given":"Alexis","family":"Giral","affiliation":"Systemethix , Australia"},{"name":"Philip S. Yu","slug":"philip-s-yu","given":"Philip S.","family":"Yu","affiliation":"Department of Computer Science , University of Illinois Chicago , USA"}],"abstract":"Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs.","doi":"10.24963/ijcai.2024/903","url":"https://ijcai.org/proceedings/2024/903","pdf":"https://ijcai.org/proceedings/2024/0903.pdf","markdown":"https://ijcai.org/proceedings/2024/903.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/903","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":"Survey Track","track":"Survey Track","number":903,"pages":{"first":8170,"last":8178},"keywords":["Data Mining: DM: Mining graphs","Humans and AI: HAI: Brain sciences"],"videos":[],"references":[{"position":1,"raw":"[Asad et al., 2023] Rimsha Asad, Saif Ur Rehman, Azhar Imran, Jianqiang Li, Abdullah Almuhaimeed, and Ab- dulkareem Alzahrani. 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