Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal Representation

TL;DR

This paper introduces a novel graph inference method that integrates domain knowledge with smooth graph signal representation, enhancing data denoising and missing data imputation in complex networks.

Contribution

It proposes a new approach to graph inference that incorporates domain-specific knowledge, improving interpretability and effectiveness over existing methods.

Findings

Improved graph signal reconstruction in district heating networks.

Enhanced denoising and missing data imputation performance.

Demonstrated benefits of domain knowledge integration.

Abstract

Graph signal processing represents an important advancement in the field of data analysis, extending conventional signal processing methodologies to complex networks and thereby facilitating the exploration of informative patterns and structures across various domains. However, acquiring the underlying graphs for specific applications remains a challenging task. While graph inference based on smooth graph signal representation has become one of the state-of-the-art methods, these approaches usually overlook the unique properties of networks, which are generally derived from domain-specific knowledge. Overlooking this information could make the approaches less interpretable and less effective overall. In this study, we propose a new graph inference method that leverages available domain knowledge. The proposed methodology is evaluated on the task of denoising and imputing missing sensor data, utilizing graph signal reconstruction techniques. The results demonstrate that incorporating domain knowledge into the graph inference process can improve graph signal reconstruction in district heating networks. Our code is available at \href{https://github.com/Keiv4n/IGL}{github.com/Keiv4n/IGL}.