Algorithm 1 Graph Summary of Large Dataset 1: Given X = {x1, ..., xn} , X = XL ∪ XU 2: Set q ← max number of subsets 3: for s ← 1, ..., q do 4: Choose a random subset with repetitions 5: After all data points are evaluated, the sample dataset X s can now be represented with the summary representative vertices as and corresponding local density constraints as, The summarization algorithm is repeated for each random subset X s , s = 1, ..., q of very large dataset X = X L ∪ X U , see Algorithm 1.
q number of summary datasets X s each of which with nb labeled data points are combined to form a representative sample of X, X = X s q s=1 reducing the number of data from n to a much smaller number of data, p = q * nb n. So the new summary of the X can be represented with X = X i p i=1 .