GEOMETRIC SCATTERING ATTENTION NETWORKS.

Area of Science:

• Graph representation learning
• Geometric scattering
• Machine learning

Background:

• Geometric scattering is increasingly recognized in graph representation learning.
• Integrating scattering features into graph convolution networks (GCNs) can mitigate node feature oversmoothing.
• Current scattering methods often require handcrafted frequency band selection and weight sharing schemes.

Purpose of the Study:

• To introduce an attention-based architecture for adaptive, task-driven node representations.
• To implicitly learn node-wise weights for combining scattering and GCN features.
• To develop the geometric scattering attention network (GSAN).

Main Methods:

• Developed a novel attention-based architecture for graph representation learning.
• Implicitly learned node-wise weights to combine multiple scattering and GCN channels.
• Utilized geometric scattering features within a GCN framework.

Main Results:

• The proposed geometric scattering attention network (GSAN) demonstrated superior performance in semi-supervised node classification tasks.
• The network effectively learned adaptive, task-driven node representations.
• Enabled spectral analysis of extracted information through node-wise attention weights.

Conclusions:

• The GSAN architecture offers an effective approach to enhance graph representation learning.
• Adaptive combination of scattering and GCN features alleviates oversmoothing and improves node classification.
• The attention mechanism provides insights into the spectral information utilized by the network.