{"type":"ScholarlyArticle","title":"Rank and Align: Towards Effective Source-free Graph Domain Adaptation","authors":[{"name":"Junyu Luo","slug":"junyu-luo","given":"Junyu","family":"Luo","affiliation":"School of Computer Science , National Key Laboratory for Multimedia Information Processing Peking University-Anker"},{"name":"Zhiping Xiao","slug":"zhiping-xiao","given":"Zhiping","family":"Xiao","affiliation":"University of California , Los Angeles"},{"name":"Yifan Wang","slug":"yifan-wang","given":"Yifan","family":"Wang","affiliation":"University of International Business and Economics"},{"name":"Xiao Luo","slug":"xiao-luo","given":"Xiao","family":"Luo","affiliation":"University of California , Los Angeles"},{"name":"Jingyang Yuan","slug":"jingyang-yuan","given":"Jingyang","family":"Yuan","affiliation":"School of Computer Science , National Key Laboratory for Multimedia Information Processing Peking University-Anker"},{"name":"Wei Ju","slug":"wei-ju","given":"Wei","family":"Ju","affiliation":"School of Computer Science , National Key Laboratory for Multimedia Information Processing Peking University-Anker"},{"name":"Langechuan Liu","slug":"langechuan-liu","given":"Langechuan","family":"Liu","affiliation":"Anker Innovations"},{"name":"Ming Zhang","slug":"ming-zhang","given":"Ming","family":"Zhang","affiliation":"School of Computer Science , National Key Laboratory for Multimedia Information Processing Peking University-Anker; Embodied AI Lab , Peking University , Beijing"}],"abstract":"Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to privacy and storage concerns. To this end, we investigate an underexplored yet practical problem of source-free graph domain adaptation, which transfers knowledge from source models instead of source graphs to a target domain. To solve this problem, we introduce a novel GNN-based approach called Rank and Align (RNA), which ranks graph similarities with spectral seriation for robust semantics learning, and aligns inharmonic graphs with harmonic graphs which close to the source domain for subgraph extraction. In particular, to overcome label scarcity, we employ the spectral seriation algorithm to infer the robust pairwise rankings, which can guide semantic learning using a similarity learning objective. To depict distribution shifts, we utilize spectral clustering and the silhouette coefficient to detect harmonic graphs, which the source model can easily classify. To reduce potential domain discrepancy, we extract domain-invariant subgraphs from inharmonic graphs by an adversarial edge sampling process, which guides the invariant learning of GNNs. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our proposed RNA.","doi":"10.24963/ijcai.2024/520","url":"https://ijcai.org/proceedings/2024/520","pdf":"https://ijcai.org/proceedings/2024/0520.pdf","markdown":"https://ijcai.org/proceedings/2024/520.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/520","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":520,"pages":{"first":4706,"last":4714},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Multi-task and transfer learning"],"videos":[],"references":[{"position":1,"raw":"[Atkins et al., 1998] Jonathan E Atkins, Erik G Boman, and Bruce Hendrickson. 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