{"type":"ScholarlyArticle","title":"Generate or Re-Weight? A Mutual-Guidance Method for Class-Imbalanced Graphs","authors":[{"name":"Zhongying Zhao","slug":"zhongying-zhao","given":"Zhongying","family":"Zhao","affiliation":""},{"name":"Gen Liu","slug":"gen-liu","given":"Gen","family":"Liu","affiliation":""},{"name":"Qi Meng","slug":"qi-meng","given":"Qi","family":"Meng","affiliation":""},{"name":"Chao Li","slug":"chao-li","given":"Chao","family":"Li","affiliation":""},{"name":"Qingtian Zeng","slug":"qingtian-zeng","given":"Qingtian","family":"Zeng","affiliation":""}],"abstract":"Class imbalance is a widespread problem in graph-structured data. The existing studies tailored for class-imbalanced graphs are typically categorized into generative and re-weighting methods. However, the former merely focuses on quantity balance rather than learning balance. The latter performs the fine-tuning in a majority-minority paradigm, overlooking the authentic-generative one. In fact, the collaboration of them is capable of relieving their respective limitations. To this end, we propose a Mutual-Guidance method for class-imbalanced graphs, namely GraphMuGu. Specifically, we first design an uncertainty-aware method to quantify the number of synthesized samples for each category. Furthermore, we devise a similarity-aware method to re-weight the importance of the authentic and generative samples. To the best our knowledge, the proposed GraphMuGu is the first try to incorporate the generative and re-weighting methods into a unified framework. The experimental results on five class-imbalanced datasets demonstrate the superiority of the proposed method. The source codes are available at https://github.com/ZZY-GraphMiningLab/GraphMuGu.","doi":"10.24963/ijcai.2025/409","url":"https://ijcai.org/proceedings/2025/409","pdf":"https://ijcai.org/proceedings/2025/0409.pdf","markdown":"https://ijcai.org/proceedings/2025/409.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/409","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Data Mining","track":"Main Track","number":409,"pages":{"first":3680,"last":3688},"keywords":["Data Mining: DM: Mining graphs","Data Mining: DM: Class imbalance and unequal cost"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}