> Which is the difference between these two functions? > > static.power.law.game > barabasi.game > static.power.law.game is a non-growing model; you give it the desired number of nodes and edges and then it will generate a graph for you whose degree distribution should be a power-law _in the infinite limit_. (It won't be if the number of nodes is relatively small, or if the number of edges is relatively large compared to the number of nodes). Under the hood, it assigns each node a "stickiness score" and creates edges between nodes with a probability that is proportional to the "stickiness" of their endpoints.
barabasi.game is a growing-model. It also generates power-law degree distributions in the infinite limit, but: 1) the out-degree of each node will be the same (in the canonical form of the model) 2) you need to specify the number of nodes and the number of outbound edges _per node_ in advance 3) since it's a growing model and nodes are added along with their edges one by one, each edge will point from a node that was added _later_ towards a node that was added _earlier_. In other words, there will be a topological ordering between the nodes and the generated graph will be acyclic Which would be best for this purpose? > It depends on the process that you want to model. If your process is a growing one, the BA model is probably a better choice. If your process is non-growing and / or there is no directionality in the relations, use the static power-law model. T.
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