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Consider the problem of the propagation of an SIS infection in two types of networks: An aggregated network , constructed by summing all contacts over the course of a week. A temporal network , in which each contact occurs only at the moment when it was recorded. The figure below shows the fraction of infected individuals I(t) over time in each network. The structural and epidemiological parameters are: Aggregated network : average degree \( \langle k \rangle = 12 \) second moment \( \langle k^2 \rangle = 260 \) Temporal network : effective average degree \( \langle k_{\text{eff}} \rangle = 5 \) effective second moment \( \langle k_{\text{eff}}^2 \rangle = 40 \) Model rates: transmission \( \beta = 0.04 \) recovery \( \mu = 0.02 \) Consider the approximate epidemic threshold for general networks: \[ \lambda_c = \frac{\mu}{\beta}\,\frac{\langle k\rangle}...

Consider the network below, where each edge is labeled with its edge betweenness centrality .   After applying a divisive community detection algorithm based on edge betweenness, the first split of the network yields the two communities shown below: Using only this two-community partition, what is the modularity of this division? Round your answer to two decimal places . 0.16 0.20 0.24 0.28 None of the above Original idea by: Luiza Barguil Inspired by: Arasteh, A., & Alizadeh, M. (2018). A fast divisive community detection algorithm based on edge degree betweenness centrality. Labels: Communities