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Hypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features
Yuyuan Ren1, Hong Ma2, Shuxin Liu2
1People's Liberation Army Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
Entropy (Basel, Switzerland)
|January 21, 2023
Summary
This study introduces a novel hypernetwork method for link prediction, enhancing accuracy by integrating network structure and node attributes. The TA-HLP model effectively captures high-order interactions, outperforming existing approaches.
Area of Science:
- Complex networks analysis
- Machine learning for network science
Background:
- Link prediction methods often overlook high-order network interactions and entity attribute information.
- This limitation leads to suboptimal performance in predicting missing or potential links.
Purpose of the Study:
- To propose a novel hypernetwork link prediction method (TA-HLP) that fuses network topology and attribute information.
- To effectively mine cross-modality interactions between high-order structure and attributes.
Main Methods:
- A dual-channel coder jointly learns structural and attribute features using node-level attention for structural encoding and hypergraphs for attribute refinement.
- High-order relationships are modeled via node-attribute-node feature updates, preserving semantic information.
- A hyperedge-level attention mechanism is incorporated for joint embedding to weigh node importance within hyperedges.
Main Results:
- The proposed TA-HLP method demonstrated a significant improvement in link prediction performance.
- Experiments on six datasets confirmed the method's superiority over existing techniques.
Conclusions:
- The TA-HLP method effectively integrates high-order network structure and attribute information for enhanced link prediction.
- This approach offers a more robust solution for complex network analysis.
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