Algorithm 1. Bias random walk algorithm.

Input G = (V, E, W), Len_walkLists, parameters w, p and q; Output vertex sequence lists: walkLists T = computing transition probabilities (G, p, q, w)//computing transition probabilities for every edge in the network Tnorm = normalizing T by Equation (2) G’ = (V, E, Tnorm) walkLists = {} for iter = 1 to Len_walkLists do     for every node u ∈ V do       Append u to seq       while len(seq) < w:         t = seq [-1] // getting the last node of the set seq         N(t) = sort (GetNeighbors(t, G’)) // sorting neighbor list of current vertex in alphabetic                       order         n = AliasSampling(N(t), Tnorm) //applying alias sampling with respect to the normalized             transition probabilities to select a next visiting neighbor node       Append n to seq     Append seq to walkLists return walkLists