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Fig. 4 | Applied Network Science

Fig. 4

From: A novel framework for community modeling and characterization in directed temporal networks

Fig. 4

Flow chart of the parameter identification method. Data on the temporal connections are gathered into the weighted adjacency matrix W of the integrated network. Then, a community detection algorithm is used to preliminary detect a set of communities. The community detection algorithm may be enriched by available metadata. A confidence interval for the community belief is then computed and the parameter γ is selected within this interval, on the basis the available data. Finally, fixed γ, the model parameters are identified through the solution of an optimization problem. A rADN model statistically compatible with the available data is eventually derived. The output of such a model can be used to assess and improve the performance of existing community detection algorithms or, on its own, to enrich the characterization of the system and to generate data and predictions that are compatible with the available data

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