Learning Product Graphs from Spectral Templates
Aref Einizade, Sepideh Hajipour Sardouie

TL;DR
This paper introduces a novel method for learning high-dimensional product graphs from spectral templates, significantly reducing computational complexity and applicable to various graph types, demonstrated on synthetic and real-world data.
Contribution
It proposes a new approach to learn product graphs from spectral templates without prior knowledge of graph types, addressing computational challenges in high-dimensional graph inference.
Findings
Effective learning of product graphs with reduced complexity
Applicable to multiple graph types without prior specification
Outperforms existing restricted approaches in experiments
Abstract
Graph Learning (GL) is at the core of inference and analysis of connections in data mining and machine learning (ML). By observing a dataset of graph signals, and considering specific assumptions, Graph Signal Processing (GSP) tools can provide practical constraints in the GL approach. One applicable constraint can infer a graph with desired frequency signatures, i.e., spectral templates. However, a severe computational burden is a challenging barrier, especially for inference from high-dimensional graph signals. To address this issue and in the case of the underlying graph having graph product structure, we propose learning product (high dimensional) graphs from product spectral templates with significantly reduced complexity rather than learning them directly from high-dimensional graph signals, which, to the best of our knowledge, has not been addressed in the related areas. In…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Complex Network Analysis Techniques · Functional Brain Connectivity Studies
