Scale-Free Internet Network Simulation

Scale-free Internet network simulation represents a crucial research direction in complex network studies, focusing on replicating the topological characteristics of real Internet infrastructure where a small number of nodes possess numerous connections. This simulation typically employs the Barabási-Albert model, implementing power-law degree distribution through preferential attachment mechanisms.

The implementation approach comprises three main phases: Network initialization begins with a small set of core nodes, simulating the embryonic stage of early Internet backbone networks Growth mechanism phase continuously introduces new nodes, where each newcomer establishes connections proportionally to existing nodes' degrees Preferential attachment strategy ensures highly-connected nodes accumulate more new links, creating the "rich-get-richer" Matthew effect

Key implementation parameters require careful configuration: initial network size and connection count per new node. The former determines the network's foundational architecture, while the latter controls average connection density. The evolutionary process naturally generates three node categories: few highly-connected hub nodes, moderately-connected router nodes, and numerous peripheral terminal nodes. Algorithmically, this can be implemented using adjacency matrices and degree-based probability calculations for connection establishment.

In practical applications, such simulations facilitate research on Internet robustness, virus propagation patterns, and traffic optimization strategies. As real Internet topology continuously evolves, modern simulation models incorporate dynamic mechanisms like node aging and connection removal to enhance authenticity. Code implementation often includes timestamp tracking and connection decay functions to simulate these dynamic behaviors.