1. Random walk informed community detection reveals heterogeneities in the lymph node conduits network
- Author
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Song, Solène, Senoussi, Malek, Escande, Paul, Villoutreix, Paul, Centre Interdisciplinaire de Nanoscience de Marseille (CINaM), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Marseille (I2M), and Aix Marseille Université (AMU)-École Centrale de Marseille (ECM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Social and Information Networks (cs.SI) ,FOS: Computer and information sciences ,FOS: Biological sciences ,Computer Science - Social and Information Networks ,Quantitative Biology - Quantitative Methods ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,[INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] ,Quantitative Methods (q-bio.QM) - 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.
- Published
- 2022
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