Network Model Selection Using Task-Focused Minimum Description Length

TL;DR

This paper introduces a task-focused network model selection method using minimum description length, which constructs efficient, parsimonious network models tailored to specific local predictive tasks, applicable across various data types.

Contribution

It presents a novel MDL-based framework for selecting network models that optimize local task efficiency, addressing overfitting and model stability issues.

Findings

Method effectively selects parsimonious network models.

Approach demonstrates stability and sensitivity in model selection.

Applicable across diverse tasks and data representations.

Abstract

Networks are fundamental models for data used in practically every application domain. In most instances, several implicit or explicit choices about the network definition impact the translation of underlying data to a network representation, and the subsequent question(s) about the underlying system being represented. Users of downstream network data may not even be aware of these choices or their impacts. We propose a task-focused network model selection methodology which addresses several key challenges. Our approach constructs network models from underlying data and uses minimum description length (MDL) criteria for selection. Our methodology measures efficiency, a general and comparable measure of the network's performance of a local (i.e. node-level) predictive task of interest. Selection on efficiency favors parsimonious (e.g. sparse) models to avoid overfitting and can be applied across arbitrary tasks and representations. We show stability, sensitivity, and significance testing in our methodology.