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Random walks on networks are widely used to model stochastic processes suchas search strategies, transportation problems or disease propagation. Aprominent example of such process is the guiding of naive T cells by the lymphnode conduits network. Here,we propose a general framework to find networkheterogeneities, which we define as connectivity patterns that affect therandom walk. We propose to characterize and measure these heterogeneities bydetecting communities in a way that is interpretable in terms of random walk.Moreover, we use an approximation to accurately and efficiently compute thesequantities on large networks. Finally, we propose an interactive datavisualization platform to follow the dynamics of the random walks and theircharacteristics on our datasets, and a ready-to-use pipeline for other datasetsupon download. By computing quantitative feature of random walk informedcommunities detected within the network, we show that the lymph node conduitnetwork is spatially coherent, however, despite its quasi-regularity, containssome random walk related heterogeneities. To evaluate these characteristics, weapplied the same workflow of diffusion based community detection and analysison the LNCN and a series of generated toy networks.

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