Random Network Generation Code

In computer science, generating random networks refers to using probabilistic models to create random graphs with specified numbers of nodes and edges. This method is commonly used to study network properties and behaviors, with widespread applications in social network analysis and search engine optimization. Various algorithms exist for generating random networks, including: Erdős-Rényi model, Watts-Strogatz model, and Barabási-Albert model. These models employ different probability distributions during network generation, resulting in distinct network structures and characteristics, making random network generation a highly active research field. The Erdős-Rényi model implementation typically uses a binomial distribution where each possible edge is included with probability p. Code implementation involves iterating through all possible node pairs and adding edges based on random number comparisons with threshold p. The Watts-Strogatz model creates small-world networks through a rewiring process. Implementation starts with a regular ring lattice where each node connects to k nearest neighbors, then rewires edges with probability β while avoiding duplicates and self-connections. The Barabási-Albert model generates scale-free networks using preferential attachment. Code implementation begins with a small connected network, then adds new nodes that connect to existing nodes with probabilities proportional to their degrees, requiring efficient degree tracking and probability calculations.