Random walk informed community detection reveals heterogeneities in the lymph node conduits network

TL;DR

This paper introduces a framework using random walk-based community detection to identify heterogeneities in the lymph node conduit network, revealing spatial coherence and localized irregularities.

Contribution

It presents a novel, interpretable method for detecting network heterogeneities via random walk communities, with an efficient approximation for large networks and an interactive visualization tool.

Findings

Lymph node conduit network is spatially coherent.

Contains localized heterogeneities despite regularity.

Method effective on both real and toy networks.

Abstract

Random walks on networks are widely used to model stochastic processes such as search strategies, transportation problems or disease propagation. A prominent example of such process is the guiding of naive T cells by the lymph node conduits network. Here,we propose a general framework to find network heterogeneities, which we define as connectivity patterns that affect the random walk. We propose to characterize and measure these heterogeneities by detecting communities in a way that is interpretable in terms of random walk. Moreover, we use an approximation to accurately and efficiently compute these quantities on large networks. Finally, we propose an interactive data visualization platform to follow the dynamics of the random walks and their characteristics on our datasets, and a ready-to-use pipeline for other datasets upon download. By computing quantitative feature of random walk informed communities detected within the network, we show that the lymph node conduit network is spatially coherent, however, despite its quasi-regularity, contains some random walk related heterogeneities. To evaluate these characteristics, we applied the same workflow of diffusion based community detection and analysis on the LNCN and a series of generated toy networks.