Semantic Information Enhanced Network Embedding with Completely Imbalanced Labels
The problem of data incompleteness has become an intractable problem for network representation learning(NRL) methods, which makes existing NRL algorithms fail to achieve the expected results.Despite numerous efforts have done to solve the issue, most of previous methods mainly focused on the lack o...
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Veröffentlicht in: | Ji suan ji ke xue 2022-11, Vol.49 (11), p.109-116 |
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Format: | Artikel |
Sprache: | chi |
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Zusammenfassung: | The problem of data incompleteness has become an intractable problem for network representation learning(NRL) methods, which makes existing NRL algorithms fail to achieve the expected results.Despite numerous efforts have done to solve the issue, most of previous methods mainly focused on the lack of label information, and rarely consider data imbalance phenomenon, especially the completely imbalance problem that a certain class labels are completely missing.Learning algorithms to solve such problems are still explored, for example, some neighborhood feature aggregation process prefers to focus on network structure information, while disregarding relationships between attribute features and semantic features, of which utilization may enhance representation results.To address the above problems, a semantic information enhanced network embedding with completely imbalanced labels(SECT)method that combines attribute features and structural features is proposed in this paper.Firstly, SECT introduces attention mech |
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ISSN: | 1002-137X |
DOI: | 10.11896/jsjkx.210900101 |