Predicting the Robustness of Large Real-World Social Networks Using a Machine Learning Model

Figure 2

(a) An example of a decision tree: the root node containing 48 networks splits into two other nodes, Node 11 and Node 12, with 13 and 35 networks, respectively, according to the value of the scale-free exponent α. Then, Node 12 splits into two other nodes, Node 21 and Node 22, with 15 and 20 networks, respectively, according to the value of the network density <k>. We assume that at Node 11, Node 21, and Node 22, no split is possible because of a certain stopping rule, and thus, they become final leaves. In general, any NSI can be used to divide networks at any split, and the decision tree can be arbitrarily complex depending on the stopping rule. (b) An illustration of the same decision tree in the 2-dimension (α and <k>) space with final leaves.

(a)

(b)