THE TRUE GAME OF THRONES 2018 CODE
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THE TRUE GAME OF THRONES 2018 TV

pagerank_numpy ( G, weight = 'weight' ). key = lambda x : x, reverse = True ) > page_rank_weighted = sorted ( nx. Still not convinced about the characters' importance? A third measure is examined, that is the Pagerank centrality, which is a centrality measured originally used by Google. Now Robert Baratheon stands out to be the most important character, Robb Stark is suddenly found in fourth place and once again Tyrion Lannister is third. Though the unweighted betweenness centrality seems to have no difference to that of the degree centrality, several characters change rank based on the weighted version of the measure. The new bar plots with the importance of the top 10 characters are now given below. betweenness_centrality ( G, weight = 'weight' ). key = lambda x : x, reverse = True ) > betweenness_weighted = sorted ( nx. The betweenness centrality for each node is the number of the shortest paths that pass through the node. Let us look at a second measure the betweenness centrality, both weighted and unweighted. But several measures exist, what is the difference in value between them? Thereupon, I will be using the library matplotlib to illustrate the results.īased on degree centrality, Eddard Stark appears to be the central character followed by Robert Baratheon, Tyrion Lannister, Catelyn Stark and Jon Snow.

THE TRUE GAME OF THRONES 2018 CODE

The code above returns a list of tuples with the name of the ten most central characters based on degree centrality. key = lambda x : x, reverse = True ) central_characters Degree centrality, corresponds to the number of links connecting it to another node. In order to verify the most central characters several centrality measures, available within networkx, will be used. In graph theory and network analysis, this can be thought as the most central node. The first question that arises is who is the central character of a Game of Thrones? Networkx allow us to study the network structure and provides several analysis measures. Thus, creating the graph becomes trivial: > import networkx as nx > G = nx. For that I will make use of another Python library specialising in the creation and manipulation of networks, called networkx. Now that the data has been loaded all I have to do is create the network. head () Source Target Type weight book 0 Addam - Marbrand Jaime - Lannister Undirected 3 1 1 Addam - Marbrand Tywin - Lannister Undirected 6 1 2 Aegon - I - Targaryen Daenerys - Targaryen Undirected 5 1 3 Aegon - I - Targaryen Eddard - Stark Undirected 4 1 4 Aemon - Targaryen - ( Maester - Aemon ) Alliser - Thorne Undirected 4 1 read_csv ( '/data/asoiaf-book1-edges.csv' ) > a_game_of_thrones. Using the Python library pandas I load the data of the first book using the following command: > import pandas as pd > a_game_of_thrones = pd. For now I will consider only the first book of the series, a Game of Thrones. Once I got my hands on these data sets I decided to go ahead and perform a very brief analysis.

The data is open and accessible on Beveridge’s Github profile. An edge weight was also applied based on the number of interactions of the characters. If two character names (or nicknames) appeared within 15 words an edge is added to connect these two characters. Each node of the network corresponds to a character whose name (or nickname) is mentioned within the book.

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In their work they created a network for each of the five books in the series of a song of Ice and Fire. It is built upon the article Networks of Thrones, by Andrew Beveridge and Jie Shan. the book series a song of Ice and Fire.This blog post is based on a lightning talk that I delivered earlier this month in a PGR meeting at Cardiff University and it is about 3 things that fascinate me! These are:

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(The following blog contains spoilers for the TV series Game of Thrones)

The true game of thrones 2018