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Title: Non-Local Graph Embedding for Multi-Scale Data Analysis

Jincheng Zhang
Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)
Advanced Graph Neural Networks

Abstract

This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local analysis, restricting the scope of investigation to individual nodes or small clusters. This work proposes a technique that allows for a more comprehensive understanding of the data's structure by representing the data as a network of interconnected nodes, enabling the analysis of relationships that extend beyond immediate neighbors. We demonstrate the effectiveness of this method through a series of quantitative and qualitative analyses, showcasing its ability to uncover hidden patterns and dependencies across multiple scales. The core mechanism involves constructing a meta-graph, where nodes represent data points and edges represent relationships. This facilitates the modeling of complex network structures and the identification of long-range connections.

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