Java: Data Science Made Easy

Negative sampling

Before we compute another set of features, the edge features, we need to first specify which edges we would like to take for that. So we need to select a set of candidate edges, and then we will train a model on them for predicting whether an edge should belong to the graph or not. In other words, we first need to prepare a dataset where existent edges are treated as positive examples, and nonexistent ones as negative.

Getting positive examples is simple: we just take all the edges and assign them the label 1.

For negative examples, it is more complex: in any real-life graph, the number of positive examples is a lot smaller than the number of negative examples. So we need to find a way to sample the negative examples so ...