# Supervised Multiscale Dimension Reduction for Spatial Interaction Networks

## TL;DR

This paper presents spinlets, a multiscale supervised dimension reduction method for spatial interaction network data, using an empirical Bayes approach with a tree-guided partitioning to improve interpretability and relevance to responses.

## Contribution

It introduces a novel multiscale Bayesian dimension reduction technique for spatial interaction networks, incorporating a tree-structured prior and an inverse Poisson model.

## Key findings

- Effective in producing compact, interpretable representations
- Applied successfully to soccer analytics data
- Enhances understanding of complex spatial networks

## Abstract

We introduce a multiscale supervised dimension reduction method for SPatial Interaction Network (SPIN) data, which consist of a collection of spatially coordinated interactions. This type of predictor arises when the sampling unit of data is composed of a collection of primitive variables, each of them being essentially unique, so that it becomes necessary to group the variables in order to simplify the representation and enhance interpretability. In this paper, we introduce an empirical Bayes approach called spinlets, which first constructs a partitioning tree to guide the reduction over multiple spatial granularities, and then refines the representation of predictors according to the relevance to the response. We consider an inverse Poisson regression model and propose a new multiscale generalized double Pareto prior, which is induced via a tree-structured parameter expansion scheme. Our approach is motivated by an application in soccer analytics, in which we obtain compact vectorial representations and readily interpretable visualizations of the complex network objects, supervised by the response of interest.