A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

Algorithm 1

Framework of the proposed algorithm.

(1) Input:

  Adjacency matrix ;

  Population size: pop;

  Maximum generation: maxgen;

  Tuning parameter: ;

(2) Step : Initialization

  (1.1) Position initialization: ;

  (1.2) Velocity initialization: ;

  (1.3) Personal best position initialization: , ;

  (1.4) Global best position initialization: select the global best position in , .

(3) Step : Iteration

  (2.1) Calculate new velocity according to (11), ;

  (2.2) Calculate new position according to (14), (15), and (16)

    ;

    if  

    Proposed strategy is adopted

    else

    Strategy in [25] is adopted

    endif

  (2.3) Function evaluation;

  (2.4) Update personal best position ;

  (2.5) Update global best position .

(4) Step : Termination Criteria

   if  maxgen is arrived

  Stop and output ;

   else

  Go back to Step ;

   end if

(5) Output: gbest.