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 threshold is compared with the 
          normalized edge centrality based on the input graph; when
          false, the  threshold is compared with the absolute edge
          centrality

_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 the
     maximum betweenness centrality. See documentation on Clustering
     algorithms in Boost Graph Library for details.

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

     A list of 

betweenness.centrality.vertices: betweenness centrality of each vertex

betweenness.centrality.edges: betweenness centrality of each edge

relative.betweenness.centrality.vertices: relative betweenness
          centrality of each vertex

dominance: central point dominance

_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:

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

     coex <- fromGXL(file(system.file("XML/conn.gxl",package="RBGL")))
     coex@edgemode <- "undirected"
     betweenness.centrality.clustering(coex)

