Against the background of globalization, city branding has become a useful strategy utilized by decision-makers in cities to promote their cities in the race for limited resources. Urban transformation factors, i.e., economic development stage and geographic position play an important role in city branding choices. We argue that city branding choices of a city can be affected by other cities through city interactions and networking. The effects of city interactions can spread across cities either through spatial proximity-referring physical or geographic connections, or logical proximity-representing city interconnectivity caused by collaboration activities. To test our argument, we develop a two-layer network model using the generalized modeling framework (GEMF) to model city branding choices, based on empirical data of sustainable brand developmental pathways from the three economic regions in China. We examine the general evolution pattern of city branding practices and construct different scenarios to analyze the impact of city interactions. The results indicate that taking city interactions as an additional factor to urban transformation allows for a better prediction on city branding choices. Policy-makers and urban planners need to consider infrastructure and economic development policies in designing city branding strategies.

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Telecommunication networks, as well as other network types, are critical infrastructures where any service disruption has a notable impact on individuals. Hence, studying network dynamics under failures or attacks is of paramount importance. In this paper, we assess the robustness of networks with respect to the spread of Susceptible-Infected-Susceptible (SIS) epidemics, using the N-Intertwined Mean-Field Approximation (NIMFA). A classical robustness metric is the NIMFA epidemic threshold, which is inversely proportional to the largest eigenvalue of the adjacency matrix, also called the spectral radius. Besides the NIMFA epidemic threshold, the viral conductance has been proposed as a measure incorporating the average fraction of infected nodes in the steady state for all possible effective infection rates. In general, the viral conductance provides more information about the network's behavior with respect to virus spreading, however, the full picture is not always necessary. The aim of this paper is to understand when the spectral radius is adequate for reflecting robustness. By analyzing the relationship between spectral radius and viral conductance in several graph classes, we show that the two metrics are highly correlated. We thus conclude that the spectral radius is sufficient to compare the robustness of networks belonging to the same class.@en

The robustness of a network is depending on the type of attack we are considering. In this paper we focus on the spread of viruses on networks. It is common practice to use the epidemic threshold as a measure for robustness. Because the epidemic threshold is inversely proportional to the largest eigenvalue of the adjacency matrix, it seems easy to compare the robustness of two networks. We will show in this paper that the comparison of the robustness with respect to virus spread for two networks actually depends on the value of the effective spreading rate ¿. For this reason we propose a new metric, the viral conductance, which takes into account the complete range of values ¿ can obtain. In this paper we determine the viral conductance of regular graphs, complete bipartite graphs and a number of realistic networks.@en