| | Input: , |
| | Output: |
| (1) | Begin |
| (2) | Initialize every node as a cluster; |
| (3) | Calculate the encounter probability and times of node m and node n in a period of time t; |
| (4) | Get Network topology (, , ) |
| (5) | Get Social relationship (, , ) |
| (6) | //Compute the node similarity |
| | First_phase: |
| (7) | Initialize (self, nodes, edges): |
| (8) | for (i = 0; i ≤ n; i++) |
| (9) | self.communities = {n1, n2, n3}; |
| (10) | partition = self.first_phase (network); |
| (11) | q = q + self.s_in[i]/2l-self.s_tot[i]/2l; |
| (12) | End for |
| (13) | Compute modularity_gain (self, node, c, k_i_in): |
| (14) | return 2 k_m_in - self.s_tot[c] self.k_m[node]/self.m; |
| (15) | If (gain > best modularity_gain) |
| (16) | best_community = community; |
| (17) | best_partition[best_community].append (node); |
| (18) | self.communities[node] = best_community; |
| (19) | End If |
| | Second_phase: |
| (20) | for (i = 0; i < partition.length; i++) |
| (21) | Self.communities = (nodes, edges); |
| (22) | In_order (nodes, edges); |
| (23) | If (modularity_gain>0) |
| (24) | return C = {C1, C2, ..., Cn}; |
| (25) | else |
| (26) | return First_phase: |
| (27) | End If |
| (28) | End for |
| (29) | END |
