Then DicoSyn.Verb has been given symmetric and reflexive.
this Markov chain is defined by the transition Matrix If ∆ 0 is the vector in V of the initial distribution probability of the walker (that is the probability to be at any vertex at the time t 0 = 0), the vector in V of the probability distribution of this walker after t steps becomes: to measure, in G = (V, E), the proximity between a vertex s ∈ V and the initial state ∆ 0 of the walker, we define prox(G, ∆ 0 , s, t) by the t steps random walk as: For a graph G = (V, E) and an initial state ∆ 0 , we can now define the flexemity at the time t of a vertex s ∈ V : the s f lex(emity) is the s prox(imity) weighted by the inverse of s degree.