A5111217835- Fire dynamics and safety research
- Advanced Computing and Algorithms
- Combustion and Detonation Processes
- Advanced Graph Neural Networks
- Risk and Safety Analysis
- Face and Expression Recognition
- High Altitude and Hypoxia
- Liver Disease Diagnosis and Treatment
- Text and Document Classification Technologies
- Privacy-Preserving Technologies in Data
- Complex Network Analysis Techniques
- Sparse and Compressive Sensing Techniques
- Thermal Regulation in Medicine
Hebei University
2024-2025
Nanjing University
2024
Fuzhou University
2024
Real-world networks frequently exhibit sparsity, which limits their ability to achieve satisfactory results. To address this issue, we propose a novel approach called nuclear norm network embedding (NNE). The is utilized as an unsupervised term in NNE enforce low-rank reconstruction space and effectively capture information of nodes with links. Furthermore, high-order (NHNE). Katz index including arbitrary proximities supervised NHNE fully extract from without links space. Extensive...
Real-world networks contain rich semantic information, making attribute network embedding a vital tool for their analysis and exploitation. Nevertheless, this process encounters significant challenges, primarily integrating heterogeneous topological information effectively managing outliers. To address these problems, we propose model called Self-supervised Attribute Network Embedding with Outliers (SANEO). Firstly, an innovative integration method is designed, which contains arbitrary...