Table 3 
GeneIdRanking With a Graph 
(1) let Q_{0 }be the probability distribution such that Q_{0}(x) = 1 
(2) for d = 1, ..., d_{max }do 
• let Q_{d}(x) = 0 for all x 
• for i = 1, ..., m do 
 sample x_{i }according to Q_{d1 } 
 Q_{d}(x_{i}) = γQ_{d1 }(x_{i}) 
 for each edge label ℓ ∈ L (x) 
* for each node y ∈ Y(x, ℓ) 
· let q_{xy }= Pr(yℓ, x)·Pr(ℓx) 
· increment Q_{d}(y) by (1  γ)Q_{d1 }(x_{i})q_{xy} 
(3) return (z) as an approximation to Q(zx) 

An efficient approximation algorithm for computing Q(zx), given transition probabilities Pr(yx, ℓ) and Pr(ℓx). 
Cohen and Minkov BMC Bioinformatics 2006 7:440 doi:10.1186/147121057440 