Fundamental Network Models for Complex Networks

This MATLAB implementation constructs fundamental network models for complex network analysis, specifically including the following models: BA (Barabási–Albert) scale-free network model, NN (Nearest Neighbor) network model, NW (Newman–Watts) small-world model, WS (Watts–Strogatz) small-world network model, and random graphs. The code implementation features algorithms for generating these network topologies through appropriate parameter settings and connection rules. Additionally, the implementation provides computational methods for analyzing key network metrics: average path length (calculated using shortest path algorithms like Dijkstra's method), clustering coefficient (measuring local connectivity through triangle counting), and degree distribution (analyzing node connectivity patterns using histogram-based statistics). These network models can be applied to study various real-world scenarios such as social networks, transportation systems, and power grids, enabling better understanding and design of complex systems. The models can be enhanced by adjusting parameters (like rewiring probability in WS model or preferential attachment in BA model), adding new nodes dynamically, or modifying connection rules to optimize network performance. It's important to note that building accurate complex network models requires deep understanding of network topology structures, node attributes, and edge weight properties. Proper implementation of these concepts enables more precise modeling and prediction of network behavior and performance characteristics through appropriate MATLAB functions and algorithmic approaches.