Basic Network features

Predictive Analytics using Networked Data in R

Bart Baesens, Ph.D.

Professor of Data Science, KU Leuven and University of Southampton

Neighborhood features

  • First order degree
    • Number of connected nodes
degree(g)

A B C D E F G H I J 
4 3 4 6 3 4 5 3 4 2

  • Second order degree
    • Number of connected nodes that are two or less edges away
neighborhood.size(g, order = 2)

7  7  9  9  8 10 10  7  8  5
Predictive Analytics using Networked Data in R

Neighborhood features - triangles

count_triangles(g)


4 3 4 7 2 3 4 2 3 1
Predictive Analytics using Networked Data in R

Centrality features

  • Betweenness

betweenness(g)

   A    B    C    D    E    F     G     H   I    J 
1.00 0.00 3.32 8.10 0.92 5.37 11.47 2.07 5.77 0.00
  • Closeness

closeness(g)

   A    B    C    D    E    F    G    H    I    J 
0.06 0.05 0.07 0.08 0.06 0.07 0.08 0.06 0.06 0.04
Predictive Analytics using Networked Data in R

Transitivity

transitivity(g,type = 'local')


0.67 1.00 0.67 0.47 0.67 0.50 0.40 0.67 0.50 1.00
Predictive Analytics using Networked Data in R

Let's practice!

Predictive Analytics using Networked Data in R

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