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Title: | MixSp : a framework for embedding heterogeneous information networks with arbitrary number of node and edge types | Authors: | Xu, L Wang, J He, L Cao, J Wei, X Yu, PS Yamanishi, K |
Issue Date: | Jun-2021 | Source: | IEEE transactions on knowledge and data engineering, June 2021, v. 33, no. 6, p. 2627-2639 | Abstract: | Heterogeneous information network (HIN) embedding is to encode network structure into node representations with the heterogeneous semantics of different node and edge types considered. However, since each HIN may have a unique nature, e.g., a unique set of node and edge types, a model designed for one type of networks may not be applicable to or effective on another type. In this article, we thus attempt to propose a framework for HINs with arbitrary number of node and edge types. The proposed framework constructs a novel mixture-split representation of an HIN, and hence is named as MixSp. The mixture sub-representation and the split sub-representation serve as two different views of the network. Compared with existing models which only learn from the original view, MixSp thus may exploit more comprehensive information. Node representations in each view are learned by embedding the respective network structure. Moreover, the node representations are further refined through cross-view co-regularization. The framework is instantiated in three models which differ from each other in the co-regularization. Extensive experiments on three real-world datasets show MixSp outperforms several recent models in both node classification and link prediction tasks even though MixSp is not designed for a particular type of HINs. | Keywords: | Heterogeneous information networks Link prediction Multi-label classification Network embedding |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on knowledge and data engineering | ISSN: | 1041-4347 | EISSN: | 1558-2191 | DOI: | 10.1109/TKDE.2019.2955945 | Rights: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication L. Xu et al., "MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 6, pp. 2627-2639, 1 June 2021 is available at https://doi.org/10.1109/TKDE.2019.2955945. |
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Xu_Mixsp_Framework_Embedding.pdf | Pre-Published version | 4.36 MB | Adobe PDF | View/Open |
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