Abstract
Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.
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Acknowledgements
This work was supported in part by the United States Agency for International Development under the Cooperative Agreement no. AID-OAA-L-15-00001, Feed the Future Innovation Laboratory for Integrated Pest Management, AgAID grant no. 2021-67021-35344 from the USDA NIFA, grant no. 2019-67021-29933 from the USDA NIFA, UVA Strategic Investment Fund SIF160, NSF Expeditions in Computing Grant CCF-1918656, and OAC-1916805 (CINES). We thank the reviewers for providing valuable suggestions for revising the paper.
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Mishra, R., Eubank, S., Nath, M., Amundsen, M., Adiga, A. (2023). Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_21
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