{"type":"ScholarlyArticle","title":"ActiveHNE: Active Heterogeneous Network Embedding","authors":[{"name":"Xia Chen","slug":"xia-chen","given":"Xia","family":"Chen","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China"},{"name":"Guoxian Yu","slug":"guoxian-yu","given":"Guoxian","family":"Yu","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China; CEMSE , King Abdullah University of Science and Technology , Thuwal , SA"},{"name":"Jun Wang","slug":"jun-wang","given":"Jun","family":"Wang","affiliation":"College of Computer and Information Sciences , Southwest University , Chongqing , China"},{"name":"Carlotta Domeniconi","slug":"carlotta-domeniconi","given":"Carlotta","family":"Domeniconi","affiliation":"Department of Computer Science , George Mason University , VA , USA"},{"name":"Zhao Li","slug":"zhao-li","given":"Zhao","family":"Li","affiliation":"Alibaba Group , Hangzhou , China"},{"name":"Xiangliang Zhang","slug":"xiangliang-zhang","given":"Xiangliang","family":"Zhang","affiliation":"CEMSE , King Abdullah University of Science and Technology , Thuwal , SA"}],"abstract":"Heterogeneous network embedding (HNE) is a challenging task due to the diverse node types and/or diverse relationships between nodes. Existing HNE methods are typically  unsupervised.  To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).In DHNE, we introduce a novel semi-supervised heterogeneous network embedding method based on graph convolutional neural network. In AQHN, we first introduce three active selection strategies based on uncertainty and representativeness, and then derive a batch selection method that assembles these strategies using a multi-armed bandit mechanism. ActiveHNE aims at improving the performance of HNE by feeding the most valuable supervision obtained by AQHN into DHNE. Experiments on public datasets demonstrate the effectiveness of ActiveHNE and its advantage on reducing the query cost.","doi":"10.24963/ijcai.2019/294","url":"https://ijcai.org/proceedings/2019/294","pdf":"https://ijcai.org/proceedings/2019/0294.pdf","markdown":"https://ijcai.org/proceedings/2019/294.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/294","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 A-L","track":"Main track","number":294,"pages":{"first":2123,"last":2129},"keywords":["Machine Learning Applications: Networks","Machine Learning: Deep Learning","Machine Learning: Semi-Supervised Learning","Machine Learning: Active Learning"],"videos":[],"references":[{"position":1,"raw":"[Cai et al., 2017a] Hongyun Cai, Vincent W. 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