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Heterogeneous-attributes enhancement deep framework for network embedding 被引量:1

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摘要 Network embedding,which targets at learning the vector representation of vertices,has become a crucial issue in network analysis.However,considering the complex structures and heterogeneous attributes in real-world networks,existing methods may fail to handle the inconsistencies between the structure topology and attribute proximity.Thus,more comprehensive techniques are urgently required to capture the highly non-linear network structure and solve the existing inconsistencies with retaining more information.To that end,in this paper,we propose a heterogeneous-attributes enhancement deep framework(HEDF),which could better capture the non-linear structure and associated information in a deep learningway,and effectively combine the structure information of multi-views by the combining layer.Along this line,the inconsistencies will be handled to some extent and more structure information will be preserved through a semi-supervised mode.The extensive validations on several real-world datasets show that our model could outperform the baselines,especially for the sparse and inconsistent situation with less training data.
出处 《Frontiers of Computer Science》 SCIE EI CSCD 2021年第6期121-131,共11页 中国计算机科学前沿(英文版)
基金 This research was partially supported by the National Natural Science Foundation of China(Grants Nos.U1605251 and 61727809).
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