  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.
