{"type":"ScholarlyArticle","title":"DANE: Domain Adaptive Network Embedding","authors":[{"name":"Yizhou Zhang","slug":"yizhou-zhang","given":"Yizhou","family":"Zhang","affiliation":"School of Electronic Engineering and Computer Science , Peking University"},{"name":"Guojie Song","slug":"guojie-song","given":"Guojie","family":"Song","affiliation":"School of Electronic Engineering and Computer Science , Peking University; Key Laboratory of Machine Perception (Ministry of Education) , Peking University"},{"name":"Lun Du","slug":"lun-du","given":"Lun","family":"Du","affiliation":"School of Electronic Engineering and Computer Science , Peking University; Key Laboratory of Machine Perception (Ministry of Education) , Peking University"},{"name":"Shuwen Yang","slug":"shuwen-yang","given":"Shuwen","family":"Yang","affiliation":"School of Electronic Engineering and Computer Science , Peking University"},{"name":"Yilun Jin","slug":"yilun-jin","given":"Yilun","family":"Jin","affiliation":"School of Electronic Engineering and Computer Science , Peking University"}],"abstract":"Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it is important to design a network embedding algorithm that supports downstream model transferring on different networks, known as domain adaptation. In this paper, we propose a novel Domain Adaptive Network Embedding framework, which applies graph convolutional network to learn transferable embeddings. In DANE, nodes from multiple networks are encoded to vectors via a shared set of learnable parameters so that the vectors share an aligned embedding space. The distribution of embeddings on different networks are further aligned by adversarial learning regularization. In addition, DANE's advantage in learning transferable network embedding can be guaranteed theoretically. Extensive experiments reflect that the proposed framework outperforms other state-of-the-art network embedding baselines in cross-network domain adaptation tasks.","doi":"10.24963/ijcai.2019/606","url":"https://ijcai.org/proceedings/2019/606","pdf":"https://ijcai.org/proceedings/2019/0606.pdf","markdown":"https://ijcai.org/proceedings/2019/606.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/606","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning M-Z","track":"Main track","number":606,"pages":{"first":4362,"last":4368},"keywords":["Machine Learning: Data Mining","Machine Learning Applications: Networks"],"videos":[],"references":[{"position":1,"raw":"[Alam et al., 2018] Firoj Alam, Shafiq R. 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