Exploring novel key regulators in breast cancer network - Fig 2

<p>Showing the Degree distribution i.e P(k) vs. k graph, After Knock out experiment at 0, 10, 20, 30, 40, 50, 100 nodes removal and it is also fitted to the power law with exponent <i>γ</i> falling in range of Characteristic Heirarchial Networks i.e. (0 ≤ <i>γ</i> ≤ 2); Showing the Clustering Co-efficient i.e C(k) vs. k graph; After Knock out experiment at 0, 10, 20, 30, 40, 50, 100 nodes removal and it is also fitted to the power law with exponent <i>α</i> falling in range of Characteristic Heirarchial Networks i.e. (<i>α</i> ~ 1); Showing the Avg Neibourhood Connectivity i.e Cn(k) vs. k graph; After Knock out experiment at 0, 10, 20, 30, 40, 50, 100 nodes removal and it is also fitted to the power law with exponent <i>β</i> falling in range of Characteristic Heirarchial Networks i.e. (<i>β</i> ≤ 1), Showing the Betweeness Centrality, Closeness Centrality and Eigenvector Centrality i.e <i>C</i>_<i>b</i>, <i>C</i>_<i>c</i> and <i>C</i>_<i>e</i> vs. <i>k</i> graph respectively, After Knock out experiment at 0, 10, 20, 30, 40, 50, 100 nodes removal and it is also fitted to the power law with exponent <i>ϵ</i>, <i>δ</i> and <i>μ</i> in order.