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Meta-path and context-aware learning for attribute completion in heterogeneous graphs
Geng Chen1, Yuan Feng1, Lijun Zhang1
1National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, 710072, China.
Summary
This study introduces EMC-Net, a new framework for heterogeneous graph attribute completion (HGAC). EMC-Net improves accuracy and efficiency by using meta-path information and context-aware learning.
Area of Science:
- Artificial Intelligence
- Data Science
- Graph Analytics
Background:
- Heterogeneous graph attribute completion (HGAC) is crucial for graph-based applications.
- Current HGAC methods neglect meta-path information and neighbor-only focus.
- Existing methods face inefficiencies with heterogeneous graph neural networks.
Purpose of the Study:
- To propose EMC-Net, a novel framework for HGAC.
- To incorporate meta-path information and context-aware learning into HGAC.
- To enhance accuracy, efficiency, and scalability of HGAC.
Main Methods:
- Developed EMC-Net, a learning framework for HGAC.
- Employed collaborative meta-path-driven embedding schemes.
- Introduced context-aware attention mechanisms for dynamic node/edge importance.
Main Results:
- EMC-Net significantly outperforms existing HGAC methods.
- Demonstrated improvements in both accuracy and computational efficiency.
- Validated through extensive experiments on benchmark datasets.
Conclusions:
- EMC-Net offers a robust and effective solution for HGAC.
- The framework provides new insights for developing advanced HGAC technologies.
- Highlights the importance of meta-path and context-aware learning in HGAC.
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