HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning
Zhuo Xu, Lu Bai, Lixin Cui, Ming Li, Yue Wang, Edwin R. Hancock·May 23, 2024
Summary
The paper introduces a novel Hierarchical Cluster-based Graph Auto-Encoder (HC-GAE) for graph representation learning. It addresses the over-smoothing issue in traditional GAEs by hierarchically clustering nodes into subgraphs during encoding, which limits information propagation. HC-GAE uses hard and soft node assignments for encoding and decoding, respectively, to capture bidirectional hierarchical features. The model combines local and global losses in a modified loss function, improving performance in node and graph classification tasks on real-world datasets like Cora, CiteSeer, PubMed, and Amazon-Computers. HC-GAE consistently outperforms existing methods, demonstrating its effectiveness in mitigating topological issues and enhancing generalization in graph analysis.
Introduction
Background
Overview of graph representation learning
Challenges with traditional GAEs, specifically over-smoothing
Objective
To address over-smoothing in GAEs
Introduce HC-GAE for improved performance in node and graph classification tasks
Method
Data Collection
Node and graph data acquisition
Real-world datasets: Cora, CiteSeer, PubMed, Amazon-Computers
Data Preprocessing
Hierarchical clustering of nodes
Node assignment strategies (hard and soft)
Encoding Process
Node clustering into subgraphs
Hard node assignments for preserving local structure
Aggregation of features within subgraphs
Decoding Process
Soft node assignments for capturing bidirectional hierarchical features
Fusion of local and global information
Loss Function
Modified loss function combining local and global losses
Minimizing reconstruction error and preserving graph structure
Experiments
Performance Evaluation
Node classification accuracy
Graph classification accuracy
Comparison with state-of-the-art methods
Over-smoothing Mitigation
Analysis of information propagation and preservation
Visualizations to demonstrate improved generalization
Hyperparameter Analysis
Sensitivity to different clustering levels and learning rates
Conclusion
Summary of HC-GAE's effectiveness in addressing over-smoothing
Advantages in node and graph classification tasks
Future research directions and potential applications
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does HC-GAE address the over-smoothing issue in traditional GAEs?
What type of losses does the modified loss function in HC-GAE combine for improved performance?
In which real-world datasets does HC-GAE demonstrate its effectiveness compared to existing methods?
What is the primary focus of the Hierarchical Cluster-based Graph Auto-Encoder (HC-GAE) paper?