Decimated Framelet System on Graphs and Fast G-Framelet Transforms

Understand

Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis.

• An adequate representation of graph data is vital to the learning performance of a statistical or machine learning model for graph-structured data.
• In this paper, we propose a novel multiscale representation system for graph data, called decimated framelets, which form a localized tight frame on the graph.
• The decimated framelet system allows storage of the graph data representation on a coarse-grained chain and processes the graph data at multi scales where at each scale, the data is stored at a subgraph.