ONA - A Song of Ice and Fire - HR Analytics live

ONA - A Song of Ice and Fire
Ricardo Nardaci
18 March 2019
The objective of this markdown is to make an Organizational Network Analysis based on the book “A song of
ice and fire” by R.R.Martin using some graph theory elements to explore more about the characters
connection.
PS: the database does not have information about all books, it’s just a subset of it with 50 edges.

A Song of Ice and Fire

      Libraries
      Creating graph structure
      Analysing Network
             Centrality degrees
             Clustering nodes
             Keyplayer and central characters
      Visualization
      Conclusion

Libraries
The following packages will be used for our Organizational Network Analysis(ONA):
tidygraph - Used for creating and manipulating graph structures;
ggraph - Used for visualization of graph structures;
tidyverse - Used for reading data and manipulating data;
DT - Used for creating friendly HTML datatable.

 library(tidygraph)
 library(ggraph)
 library(tidyverse)
 library(DT)

Creating graph structure
With the use of tidygraph, we can easily create a graph structure as well as get it back to a tibble or data
frame object. First we will create the graph object so we can make our analysis and get it back to data frame
for easier manipulation and visualization.

edges%
   activate(edges) %>%
   filter(!edge_is_multiple()) %>%
   mutate(centrality_e = centrality_edge_betweenness())

 df%
 activate(nodes) %>%
 as_tibble()%>%
 data.frame()

So we can dismember this all-in-one graph in centrality degree, distance to center node, clustering and two
logical variables(is it center node? and is it keyplayer node?).

Centrality degrees
Centrality degrees is the number of neighbors a node(in this case, a node is a character) has, so it can show
us who is the most influent as influent characters will have more neighbors than the ordinaries. A good way of
visualizing it is arranging the neighbors of each character in descending order.

 datatable(df%>%
           arrange(desc(Neighbors))%>%
           select(c('name','Neighbors')),filter='top',rownames=F,
                 options=list(dom='ltipr'))

Show 10          entries

                           name                                                                  Neighbors

    All                                                     All

 Tyrion-Lannister                                                                                         36

 Robert-Baratheon                                                                                         33

 Joffrey-Baratheon                                                                                         32

 Cersei-Lannister                                                                                         30

 Eddard-Stark                                                                                             30

 Jaime-Lannister                                                                                          29

 Sansa-Stark                                                                                              29

 Robb-Stark                                                                                               27

 Stannis-Baratheon                                                                                        27

 Catelyn-Stark                                                                                            26

Showing 1 to 10 of 50 entries                            Previous      1       2     3    4      5     Next

Clustering nodes
Clustering nodes can reveal us(specially when visualizing the graph) groups that are well related between
them. We can get the family name of each character to see if families dominate clusters as it is expected on
Song of Ice and Fire.

 df$cont_sep

name                               Family                                 Group

     All                                  All                                   All

 Joffrey-Baratheon                      Baratheon                            1

 Myrcella-Baratheon                    Baratheon                            1

 Renly-Baratheon                       Baratheon                            1

 Robert-Baratheon                      Baratheon                            1

 Stannis-Baratheon                     Baratheon                            5

 Tommen-Baratheon                      Baratheon                            1

 Rodrik-Cassel                         Cassel                               2

 Gregor-Clegane                        Clegane                              1

Showing 1 to 10 of 50 entries                             Previous      1       2     3      4     5     Next

Keyplayer and central characters
Keyplayer characters are exceptional characters normally those characters can create bridges between
clusters or peripheral characters, without them it would be harder to share thoughts, in this analysis we picked
up the top10 keyplayers. We can simply visualize the keyplayers and the central characters in a table.

 datatable(df%>%
            filter(Keyplayer==T | Center==T)%>%
            select(c('name','Center','Keyplayer')),filter='top',rownames=F,
            options=list(dom='ltipr'))

Show 100        entries

                 name                               Center                                Keyplayer

     All                                  All                                   All

 Aemon-Targaryen-(Maester-
                                       false                                true
 Aemon)

 Catelyn-Stark                         false                                true

 Cersei-Lannister                      true                                 false

 Drogo                                 false                                true

 Eddard-Stark                          true                                 false

 Hizdahr-zo-Loraq                      false                                true

name                              Center                             Keyplayer

    All                                  All                                 All

 Hodor                                false                               true

 Joffrey-Baratheon                     true                                true

 Jon-Snow                             false                               true

 Pycelle                              false                               true

 Robert-Baratheon                     true                                false

 Samwell-Tarly                        false                               true

 Sandor-Clegane                       false                               true

 Sansa-Stark                          true                                false

 Tyrion-Lannister                     true                                false

Showing 1 to 15 of 15 entries                                                      Previous    1     Next

Visualization
For visualizing the connections with a graph layout we will use the ggraph package. The connections(or
edges) between the nodes will be larger as the weight increases and each node will have its character name
as a label and will be coloured from it respective cluster.

 layout

As seen in the tables above the keycharacters connect groups(and maybe in this graph we can filter it to a
lesser group instead of 10) and the clusters are all well distributed

Conclusion
The ONA is a great solution for a organizational analysis, it’s a trending topic in HR analytics as it can show
some keyplayer employees and reveal those peripheral employees that can be great developers and don’t
pass their knowledge forward, it’s a great tool for managers so they can know better their team, discover
essential employees for their team and even help them in decision making besides some of the information
from this ONA can even be helpful when used in models for prediction.

Although there’s a lot more to explore on this area, I hope you enjoyed this analysis.