Algorithm to extract features from the graph with the node2vec(word2vec) algorithm according to [https://arxiv.org/pdf/1607.00653v1.pdf].
This algorithm could take a lot of time on large networks (many nodes).
G (networkit.Graph) – The graph.
P (float) – The ratio for returning to the previous node on a walk. For P > max(Q,1) it is less likely to sample an already-visited node in the following two steps. For P < min(Q,1) it is more likely to sample an already-visited node in the following two steps.
Q (float) – The ratio for the direction of the next step For Q > 1 the random walk is biased towards nodes close to the previous one. For Q < 1 the random walk is biased towards nodes which are further away from the previous one.
L (int) – The walk length.
N (int) – The number of walks per node.
D (int) – The dimension of the calculated embedding.
Returns all feature vectors
A vector containing feature vectors of all nodes