betweenness.centrality.clustering    package:RBGL    R Documentation

_G_r_a_p_h _c_l_u_s_t_e_r_i_n_g _b_a_s_e_d _o_n _e_d_g_e _b_e_t_w_e_e_n_n_e_s_s _c_e_n_t_r_a_l_i_t_y

_D_e_s_c_r_i_p_t_i_o_n:

     Graph clustering based on edge betweenness centrality

_U_s_a_g_e:

     betweenness.centrality.clustering(g, threshold = -1, normalize = T)

_A_r_g_u_m_e_n_t_s:

       g: an instance of the 'graph' class with 'edgemode' "undirected"

threshold: threshold to terminate clustering process

normalize: boolean, when TRUE, the  edge betweenness centrality is 
          scaled by '2/((n-1)(n-2))' where 'n' is the number of
          vertices  in 'g'; when FALSE, the edge betweenness centrality
          is the absolute value

_D_e_t_a_i_l_s:

     To implement graph clustering based on edge betweenness
     centrality.

     The algorithm is iterative, at each step it computes the edge
     betweenness centrality and removes the edge with maximum
     betweenness centrality when it is above the given 'threshold'. 
     When the maximum betweenness centrality  falls below the
     threshold, the algorithm terminates.

     See documentation on Clustering algorithms in Boost Graph Library
     for details.

_V_a_l_u_e:

     A list of 

no.of.edges: number of remaining edges after removal

   edges: remaining edges

edge.betweenness.centrality: betweenness centrality of remaining edges

_A_u_t_h_o_r(_s):

     Li Long <li.long@isb-sib.ch>

_R_e_f_e_r_e_n_c_e_s:

     Boost Graph Library by Siek et al.

_S_e_e _A_l_s_o:

     'brandes.betweenness.centrality'

_E_x_a_m_p_l_e_s:

     coex <- fromGXL(file(system.file("XML/conn.gxl",package="RBGL")))
     coex <- ugraph(coex)
     betweenness.centrality.clustering(coex, 0.5, TRUE)

