Research Article
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. |
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