Clustering Diffusion Model With Frequency-Signal Modulation for Variational Graph Autoencoders.

Area of Science:

• Graph Neural Networks
• Machine Learning
• Data Mining

Background:

• Variational Autoencoders (VAEs) are popular for node clustering, with research focusing on improving their latent space expressiveness.
• Integrating diffusion models with VAEs shows promise, but the underlying mechanism for performance enhancement is not well understood.

Purpose of the Study:

• To empirically analyze the mechanism of diffusion model enhancement in VAE-based node clustering using graph spectral theory.
• To propose a novel method, FVD, to address limitations of diffusion models in VAEs for node clustering.

Main Methods:

• Empirical analysis using graph spectral theory to understand diffusion model impact on VAEs.
• Development of FVD, a plug-and-play method incorporating graph wavelet transform and Student's t-distribution.
• Integration of FVD with existing VAE-based node clustering methods.

Main Results:

• Diffusion models align with low-frequency spectral characteristics of VAEs, explaining their effectiveness.
• Diffusion models struggle with high-frequency signals and capturing cluster-specific details, leading to limitations.
• FVD effectively modulates frequency bands, preserves node information, and mitigates cluster collapse, improving VAE performance.

Conclusions:

• The study clarifies the spectral mechanism behind diffusion model effectiveness in VAE node clustering.
• FVD offers a significant improvement for VAE-based node clustering by addressing diffusion model limitations.
• FVD demonstrates competitive performance gains when integrated with existing VAE methods.