Single-trajectory map equation

TL;DR

This paper introduces the single-trajectory map equation, a new formulation that offers deeper insights into community detection and reduces overfitting in network analysis.

Contribution

It revisits the original map equation, proposing a raw form based on single trajectories that enhances understanding and balances community detection.

Findings

Provides a detailed analysis of the map equation's inner workings.

Demonstrates reduced overfitting in community detection.

Offers a new perspective through the single-trajectory formulation.

Abstract

Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, which is an information-theoretic framework based on the random walk on a network, is a particularly popular community detection method. Despite its outstanding performance in many applications, the inner workings of the map equation have not been thoroughly studied. Herein, we revisit the original formulation of the map equation and address the existence of its ``raw form,'' which we refer to as the single-trajectory map equation. This raw form sheds light on many details behind the principle of the map equation that are hidden in the steady-state limit of the random walk. Most importantly, the single-trajectory map equation provides a more balanced community structure, naturally reducing the tendency of the overfitting phenomenon in the map equation.