{"type":"ScholarlyArticle","title":"Network Schema Preserving Heterogeneous Information Network Embedding","authors":[{"name":"Jianan Zhao","slug":"jianan-zhao","given":"Jianan","family":"Zhao","affiliation":"School of CS , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Xiao Wang","slug":"xiao-wang","given":"Xiao","family":"Wang","affiliation":"School of CS , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Chuan Shi","slug":"chuan-shi","given":"Chuan","family":"Shi","affiliation":"School of CS , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Zekuan Liu","slug":"zekuan-liu","given":"Zekuan","family":"Liu","affiliation":"School of CS , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Yanfang Ye","slug":"yanfang-ye","given":"Yanfang","family":"Ye","affiliation":"Department of CDS , Case Western Reserve University , OH , USA"}],"abstract":"As heterogeneous networks have become increasingly ubiquitous, Heterogeneous Information Network (HIN) embedding, aiming to project nodes into a low-dimensional space while preserving the heterogeneous structure, has drawn increasing attention in recent years. Many of the existing HIN embedding methods adopt meta-path guided random walk to retain both the semantics and structural correlations between different types of nodes. However, the selection of meta-paths is still an open problem, which either depends on domain knowledge or is learned from label information. As a uniform blueprint of HIN, the network schema comprehensively embraces the high-order structure and contains rich semantics. In this paper, we make the first attempt to study network schema preserving HIN embedding, and propose a novel model named NSHE. In NSHE, a network schema sampling method is first proposed to generate sub-graphs (i.e., schema instances), and then multi-task learning task is built to preserve the heterogeneous structure of each schema instance. Besides preserving pairwise structure information, NSHE is able to retain high-order structure (i.e., network schema). Extensive experiments on three real-world datasets demonstrate that our proposed model NSHE significantly outperforms the state-of-the-art methods.","doi":"10.24963/ijcai.2020/190","url":"https://ijcai.org/proceedings/2020/190","pdf":"https://ijcai.org/proceedings/2020/0190.pdf","markdown":"https://ijcai.org/proceedings/2020/190.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/190","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":190,"pages":{"first":1366,"last":1372},"keywords":["Data Mining: Clustering, Unsupervised Learning","Machine Learning: Deep Learning: Convolutional networks","Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Feature Extraction, Selection and Dimensionality Reduction"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25079/file_25079.m3u8","length_sec":271,"provider":"hls"}],"references":[{"position":1,"raw":"[Cui et al., 2019] Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 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