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SHNE: Semantics and Homophily Preserving Network Embedding.
IEEE Transactions on Neural Networks and Learning Systems
|October 12, 2021
Summary
This study introduces a novel Semantics and Homophily preserving Network Embedding (SHNE) model to address limitations in graph convolutional networks. SHNE enhances node embeddings by preserving semantics and homophily, outperforming existing methods.
Area of Science:
- Graph Neural Networks
- Network Embedding
- Machine Learning
Background:
- Graph convolutional networks (GCNs) face oversmoothing, limiting depth and expressive power.
- Existing methods often ignore high-order structural semantics and node homophily.
- Shallow GCN architectures restrict the capture of information beyond local neighborhoods.
Purpose of the Study:
- To propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model.
- To overcome the limitations of oversmoothing and insufficient expressive power in GCNs.
- To enhance network embedding by preserving both structural semantics and node homophily.
Main Methods:
- Leveraging higher-order connectivity patterns to capture structural semantics.
- Utilizing structural and feature similarity to identify correlated neighbors across the entire graph.
- Employing dual-attention mechanisms and a semantic regularizer for comprehensive embedding generation.
Main Results:
- The proposed SHNE model effectively captures higher-order structural semantics.
- SHNE successfully exploits node homophily by considering distant but relevant nodes.
- Dual-attention mechanisms and semantic regularization improve embedding quality.
Conclusions:
- SHNE significantly outperforms state-of-the-art methods on benchmark datasets.
- The model addresses key limitations in GCNs, including oversmoothing and information neglect.
- SHNE offers a more powerful and comprehensive approach to network embedding.
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