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"), open="r"))
     coex <- ugraph(coex)
     betweenness.centrality.clustering(coex, 0.1, TRUE)

