# Graph clustering in industrial networks

## TL;DR

This paper develops a hierarchical clustering method for industrial trade networks, confirming around ten clusters aligned with historical innovation diffusion patterns, using a spectral approach inspired by random walks.

## Contribution

It introduces a novel hierarchical clustering procedure tailored to industrial networks, combining spectral analysis with a new resorting technique, extending prior random walk-based methods.

## Key findings

- Identified approximately ten industrial clusters consistent with historical innovation patterns.
- Validated the clustering method through spectral analysis and hierarchical structure detection.
- Supported the concept of sequential waves of technological development.

## Abstract

The present work investigates clustering of a graph-based representation of industrial connections derived from international trade data by Hidalgo et al (2007) and confirms existence of around ten industrial clusters that are reasonably consistent with expected historical patterns of diffusion of innovation and technology. This supports the notion that technological development occurs in sequential innovation waves. The clustering method developed in this work follows conceptual ideas of Lambiotte and Barahona (2009), who suggested to use random walk to assess a hierarchical structure of network communities where different levels of the hierarchy correspond to different diffusion times. We, however, implement these ideas differently to match physics of the problem under consideration and introduce a hierarchal clustering procedure that is combined with convenient resorting of the elements. An equivalent spectral interpretation of the clustering is also given and discussed in the paper.