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WANG Jianxia,LIU Menglin,XU Yunfeng,ZHANG Yan.Review on heterogeneous network representation learning method[J].Journal of Hebei University of Science and Technology,2021,42(1):48-59
異構網絡表示學習方法綜述
Review on heterogeneous network representation learning method
Received:October 02, 2020  Revised:October 27, 2020
DOI:10.7535/hbkd.2021yx01007
中文關鍵詞:  計算機神經網絡  異構網絡  表示學習  圖神經網絡  建模能力
英文關鍵詞:computer neural network  heterogeneous network  representation learning  graph neural network  modeling copabilities
基金項目:中國留學基金委地方合作項目(201808130283); 中國教育部人工智能協同育人項目(201801003011); 河北科技大學校立課題(82/1182108)
Author NameAffiliationE-mail
WANG Jianxia School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
LIU Menglin School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
XU Yunfeng School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang hbkd_xyf@hebust.edu.cn 
ZHANG Yan School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
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中文摘要:
      現實生活中存在的網絡大多是包含多種類型節點和邊的異構網絡,比同構網絡融合了更多信息且包含更豐富的語義信息。異構網絡表示學習擁有強大的建模能力,可以有效解決異構網絡的異質性,并將異構網絡中豐富的結構和語義信息嵌入到低維節點表示中,以便于下游任務應用。通過對當前國內外異構網絡表示學習方法進行歸納分析,綜述了異構網絡表示學習方法的研究現狀,對比了各類別模型之間的特點,介紹了異構網絡表示學習的相關應用,并對異構網絡表示學習方法的發展趨勢進行了總結與展望,提出今后可在以下方面進行深入探討:1)避免預先定義元路徑,應充分釋放模型的自動學習能力;2)設計適用于動態和大規模網絡的異構網絡表示學習方法。
英文摘要:
      Most of the real-life networks are heterogeneous networks that contain multiple types of nodes and edges, and heterogeneous networks integrate more information and contain richer semantic information than homogeneous networks. Heterogeneous network representation learning to have powerful modeling capabilities, enables to solve the heterogeneity of heterogeneous networks effectively, and to embed the rich structure information and semantic information of heterogeneous networks into low-dimensional node representations to facilitate downstream task applications. Through sorting out and classifying the current heterogeneous network representation learning methods at home and abroad, reviewed the current research status of heterogeneous network representation learning methods, compared the characteristics of each category model , introduced the related applications of heterogeneous network representation learning, and summarized and prospected the development trend of heterogeneous network representation learning methods. It is proposed that in-depth discussion can be carried out in the following aspects in future: First, avoid predefined meta-paths and fully release the automatic learning capabilities of the model; Second, design heterogeneous network representation learning method suitable for dynamic and large-scale networks.
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