Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and K²-Tree

Algorithm 2

Cluster compression.

Input: boundary_ list to record the boundary of the cluster boundary information and remove the adjacency matrix M0 of the cluster.
Output: T1, T2, T3, …, Tn, TM0, L1, L2, L3, …, Ln, LM0;
(1)	n := boundary_list.size()/4;
(2)	for (i = 1 to n)
(3)	K^2-tree representation for each cluster i, stored with T and L vectors, denoted Ti, Li;
(4)	end for
(5)	K^2-tree representation of M0, stored by T and L vectors, denoted as TM0, LM0;
(6)	return T1, T2, T3, …, Tn, TM0, L1, L2, L3, …, Ln, LM0;