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Barabási–Albert (BA) model

Consider the following statements about the Barabási–Albert (BA) model: I. The BA model combines two mechanisms (growth and preferential attachment) to explain the emergence of hubs and a scale-free degree distribution. II. In the BA model, the degree distribution follows a power law with exponent γ = 3, independent of the parameter 𝑚. III. Without preferential attachment, but with growth alone, the resulting degree distribution is still scale-free. Which statements are correct? II and III I and III Only I  I and II None of the above. Original idea by: Heigon Soldera
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Scale-free Networks

Consider the following statements about scale-free networks: I. In scale-free networks, the variance of the degree (<k²>) may diverge when the power-law exponent is less than or equal to 3, leading to important consequences for network robustness and dynamics. II. For γ < 2, the largest hub grows proportionally to N, increasingly concentrating the network’s connections. III. All real-world networks are scale-free, since power laws are universal in complex systems. Which statements are correct? Only I I and II II and III  I and III None of the above. Original idea by: Heigon Soldera
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Real vs. Random Networks

Why can’t real-world networks (like social, biological, or technological networks) be fully described by the Erdős–Rényi random network model? Because real networks always have fewer nodes than random networks. Because real networks evolve too quickly to be modeled mathematically. Because real networks have degree distributions with hubs and outliers. Because random networks forbid clustering while real networks allow it. None of the above. Original idea by: Heigon Soldera
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