Algorithm 1: The double-layer Tabu search maximal clique (DTSMC).
	Input: $G_i^{+}$, Degree ($G_i^{+}$) and N($G_i^{+}$) Output: $C_{all}^i$
1	Initialization: randomly select the min degree vertex Current from $G_i^{+}$, Tabu_first_set = ø, Tabu_second_set = ø, $C_{all}^i$ = ø, Ci = ø, p_next = ε, P_left = $G_i^{+}$
2	for j = 1; j ≤ |P_left|; j++ do // Find maximal clique of subgraph
3	Candi_set = N($p_j^i$);
4	while |Candi_set| ≠ ø // Find the maximum clique at the current node
5	Randomly select a vertex p_next from Candi_set;
6	if p_next ∈ ${\displaystyle \underset{k\le |C^j|}{\cap }N(c_k^j)}$ // Evaluate common neighbor nodes
7	Cj ← Cj ∪ {p_next };
8	Tabu_second_set ← Tabu_second_set ∪ {p_next};    // Update second-level Tabu list
9	Candi_set ← ${\displaystyle \underset{k\le |C^j|}{\cap }N(c_k^j)}$ − Cj; //
10	end if
11	end while
12	for k = 1, K = |Cj|
13	if degree(${{\displaystyle c}}_k^j$) = |Cj| − 1 // Compute the node degree
14	P_left ← P_left/$c_k^j$; // Update second-level candidate solution list
15	Tabu_firt_set = $G_i^{+}$ − P_left; // Update first-level Tabu list
16	end if
17	end for
18	$C_{all}^i$ = $C_{all}^i$ + Cj;
19	end for
20	Return $C_{all}^i$ //Return all maximal clique