{"type":"ScholarlyArticle","title":"EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost","authors":[{"name":"Jaeseung Heo","slug":"jaeseung-heo","given":"Jaeseung","family":"Heo","affiliation":"Graduate School of Artificial Intelligence , POSTECH , South Korea"},{"name":"Seungbeom Lee","slug":"seungbeom-lee","given":"Seungbeom","family":"Lee","affiliation":"Graduate School of Artificial Intelligence , POSTECH , South Korea"},{"name":"Sungsoo Ahn","slug":"sungsoo-ahn","given":"Sungsoo","family":"Ahn","affiliation":"Graduate School of Artificial Intelligence , POSTECH , South Korea; Department of Computer Science & Engineering , POSTECH , South Korea"},{"name":"Dongwoo Kim","slug":"dongwoo-kim","given":"Dongwoo","family":"Kim","affiliation":"Graduate School of Artificial Intelligence , POSTECH , South Korea; Department of Computer Science & Engineering , POSTECH , South Korea"}],"abstract":"Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EPIC (Edit Path Interpolation via learnable Cost), a novel interpolation-based method for augmenting graph datasets. To interpolate between two graphs lying in an irregular domain, EPIC leverages the concept of graph edit distance, constructing an edit path that represents the transformation process between two graphs via edit operations. Moreover, our method introduces a context-sensitive cost model that accounts for the importance of specific edit operations formulated through a learning framework. This allows for a more nuanced transformation process, where the edit distance is not merely count-based but reflects meaningful graph attributes. With randomly sampled graphs from the edit path, we enrich the training set to enhance the generalization capability of classification models. Experimental evaluations across several benchmark datasets demonstrate that our approach outperforms existing augmentation techniques in many tasks.","doi":"10.24963/ijcai.2024/455","url":"https://ijcai.org/proceedings/2024/455","pdf":"https://ijcai.org/proceedings/2024/0455.pdf","markdown":"https://ijcai.org/proceedings/2024/455.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/455","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":"Machine Learning","track":"Main Track","number":455,"pages":{"first":4116,"last":4126},"keywords":["Machine Learning: ML: Sequence and graph learning"],"videos":[],"references":[{"position":1,"raw":"[Abu-Aisheh et al., 2015] Zeina Abu-Aisheh, Romain Raveaux, Jean-Yves Ramel, and Patrick Martineau. 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