A Semantic Community Detection Algorithm Based on Quantizing Progress

Algorithm 2

Optimization algorithm by PSO.

Input:

The semantic social network gragh disposed by LDA;

Output:

Useful transformable probability matrix;

Step  0. Initialize proper parameters, inertia weight , constriction factor , study

factors , , population size(the size of network) , particle size (the number of

nodes in semantic social network) and maximum iteration .

Step  1. Initialize all particles and let ;

Step  2. Evaluate fitness of each particle;

Step  3. Judge whether the ultimate criteria is satisfied. If , stop and jump to Final.; otherwise

refresh variables according to the following steps;

Step  4. Refresh by comparing the current fitness of each particle with its own historical best position

, if gets smaller, then change it with the current position;

Step  5. Refresh by comparing the current best fitness of all particles with the historical best

position of the whole swarm, if gets smaller, then change it with the current best position;

Step 6. Refresh and using Eq (12) and Eq (13);

Step 7. , return Step 2;

Final.