Currently the Random Network Plugin is capable of producing networks according to three different random network models:
- Erdős-Rényi Model: (http://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93R%C3%A9nyi_model)
- G(n,m): Users specify both the number of nodes and the number of edges. The algorithm randomly creates a network uniformly over all networks that have n nodes and m edges.
- G(n,p): Users specify both the number of nodes and the probability of edge creation. Each possible edge is independently created with probability p.
- Watts-Strogatz Model: (http://en.wikipedia.org/wiki/Watts_and_Strogatz_model)
- W(n,d, β): Works in two phases. In the first phase the algorithm creates a complete lattice of n nodes is created where each edge is connected to d neighbors on its left and d neighbors on its right. In the next phase edges are randomly shuffled with probability β, with the constraint that existing edges should not be replicated.
- Barabási-Albert Model: (http://en.wikipedia.org/wiki/Barabasi-Albert_model)
- B(n, i, d): Uses a preferential attachment method. First a complete initial seed network of i nodes is created. The remaining (n - i) nodes are added one at a time. d edges are drawn to existing nodes, such that the probability of selecting an existing node u, is degree(u)/ 2 *E. Where degree(u) is the number of edges incident to u and E is the number of current edges.
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