Version: 2.1.1

# Game of Thrones deaths

We highly recommend checking out the other articles from this series which are listed in our tutorial overview section.

## Introduction​

WARNING - this tutorial could contain Game of Thrones spoilers.

Game of Thrones is an American fantasy drama television series created by David Benioff and D. B. Weiss for HBO. It is an adaptation of A Song of Ice and Fire, George R. R. Martin's series of fantasy novels, the first of which is A Game of Thrones. The Game of Thrones world is full of interesting characters, locations, scenarios, unexpected deaths, and plot twists.

Even though the COVID-19 pandemic hit the entire world in 2020 and is now starting to become one of the worst years in recent history, 2019 was also a huge disappointment to all Game of Thrones fans. According to user reactions, a seven-year build-up resulted in a poorly written ending of the last season and ruined the ending of one of the most popular shows on the planet. Nonetheless, if you want to know how many characters would have survived if Jon Snow had stayed dead, which House had the best Kill/Death Ratio, or who was the biggest traitor in the show, you came to the right place!

## Data model​

Although the Game of Thrones TV show is based on a series of books, our graph database contains only characters from the previously mentioned TV shows as the books are still not finished. This tutorial would not be possible without data analyst David Murphy who shared his collection of on-screen deaths. For more information, you can visit his blogpost with interactive analysis on the show deaths. We won't be working with kills and deaths that happened off-screen or were tied to the undead (wraiths). The dataset we used was slightly modified, in which columns "Episode Name" and "IMDb Rating" were added.

The model consists of the following nodes:

• a Character node has a name attribute corresponding to the character's name (e.g. "Jon Snow")
• an Allegiance node has a name attribute corresponding to the house name the character is loyal to (e.g. "House Stark")
• a Death node has an order attribute corresponding to the order in which the death happened in the show (e.g. 602)
• an Episode node has a number attribute corresponding to the number of episodes (e.g. 10), name attribute corresponding to the name of the episode (e.g. "Mothers Mercy") and imdb_rating episode corresponding to the IMDB rating of the episode (e.g. "9.1")
• a Season node has a number attribute corresponding to the number of the season (e.g. 10)
• a Location node has a name attribute corresponding to the location's name (e.g. "Castle Black")

Nodes are connected with the following edges:

• :KILLED - connect two Character nodes and they have 2 attributes, method which says how was the character killed (e.g. "Knife") and count attribute representing a number of how many of these characters were killed (e.g. if "Jon Snow" killed 10 "Lannister soldiers" then on this edge count would be 10)
• :LOYAL_TO - connects Character node with Allegiance node representing an allegiance the character is loyal to
• :VICTIM_IN and :KILLER_IN - connects Character node with Death node in which death happened
• :HAPPENED_IN - connects node Death with Episode, Season and Location nodes representing details of the death
• :PART_OF connects node Episode with Season node which episode is part of

## Exploring the dataset​

You have two options for exploring this dataset. If you just want to take a look at the dataset and try out a few queries, open Memgraph Playground and continue with the tutorial there. Note that you will not be able to execute write operations.

On the other hand, if you would like to add changes to the dataset, download the Memgraph Lab desktop application and navigate to the Datasets tab in the sidebar. From there, choose the dataset Game of Thrones deaths and continue with the tutorial.

## Example queries using Cypher​

In the queries below, we are using Cypher to query Memgraph via the console.

Here are some queries you might find interesting:

MINI-GAME - If you have watched the TV Show, try to guess each result before executing the query!

1. Let's start with a couple of simple queries. List the locations where most deaths occurred. Can you guess which one is it?

MATCH (l:Location)<-[:HAPPENED_IN]-(d:Death)RETURN l.name AS location_name, count(d) AS death_countORDER BY death_count DESC;

2. Now that we have the location with the most deaths, let's list the episodes with the most deaths as well.

MATCH (d:Death)-[:HAPPENED_IN]->(e:Episode)RETURN e.name AS episode_name, count(d) AS kill_countORDER BY kill_count DESC;

3. List the number of kills per season. If you have watched the show, you should be able to guess this one.

MATCH (d:Death)-[:HAPPENED_IN]->(s:Season)RETURN s.number AS season_number, count(d) AS death_countORDER BY season_number ASC;

4. The most poorly rated season by far was the last one, but can you guess the best one? Let's list the seasons by IMDB ratings. The problem we get with using the avg() function is that it gives us too many decimals, therefore a useful solution is given in this example using round().

MATCH (e:Episode)-[:PART_OF]->(s:Season)RETURN s.number AS season_name, round(100 * avg(e.imdb_rating))/100 AS ratingORDER BY rating DESC;

5. There are many methods by which characters were killed such as sword or Dragonfire, but let's list victims of unique methods.

MATCH (:Character)-[k:KILLED]->(:Character)WITH k.method AS kill_method, count(k.method) AS method_countWHERE method_count < 2MATCH (killer:Character)-[k:KILLED]->(victim:Character)WHERE k.method = kill_methodRETURN kill_method, victim.name AS victim;

6. Daenerys Stormborn of House Targaryen, the First of Her Name, Queen of the Andals and the First Men, Protector of the Seven Kingdoms, the Mother of Dragons, the Khaleesi of the Great Grass Sea, the Unburnt, the Breaker of Chains or shortened to "Daenerys Targaryen" in our database is the biggest killer on the show. Let's list all the episodes she killed in as well as characters she killed.

MATCH (daenerys:Character {name: 'Daenerys Targaryen'})-[:KILLED]->(victim:Character)MATCH (daenerys)-[:KILLER_IN]->(d:Death)<-[:VICTIM_IN]-(victim)MATCH (d)-[:HAPPENED_IN]-(e:Episode)RETURN DISTINCT victim.name AS victim, count(d) AS kill_count, e.name AS episode_nameORDER BY kill_count DESC;

7. Houses or allegiances are one of the main aspects of Westeros. Some houses killed more characters than others, but that doesn't matter in the end, what matters is efficiency. Let's list the allegiances with the best Kill/Death Ratios or KDR for short. Here we came across one additional problem. If an allegiance had more deaths than kills, the KDR would be 0. This can easily be fixed with the toFloat() function.

MATCH (:Character)-[death:KILLED]->(:Character)-[:LOYAL_TO]->(a:Allegiance)WITH a, sum(death.count) AS deathsMATCH (:Character)<-[kill:KILLED]-(:Character)-[:LOYAL_TO]->(a)RETURN a.name AS allegiance_name,       sum(kill.count) AS kills,       deaths,       round(100 *(tofloat(sum(kill.count))/deaths))/100 AS KDRORDER BY KDR DESC;

8. One of the best-rated episodes, Battle of the Bastards, showed us a fight between two houses: Stark and Bolton. Let's see which one had more casualties.

MATCH (c:Character)-[:LOYAL_TO]->(a:Allegiance)MATCH (c)-[:VICTIM_IN]-(d:Death)-[:HAPPENED_IN]-(:Episode {name: 'Battle of the Bastards'})RETURN a.name AS house_name, count(d) AS death_countORDER BY death_count DESCLIMIT 2;

9. One of the biggest features of Memgraph is drawing the graphs of queries we execute. Let's visualize all the Loyalties with Characters. Execute the following query and head out to the GRAPH tab.

MATCH (character:Character)-[loyal_to:LOYAL_TO]-(allegiance)RETURN character, loyal_to, allegiance;

10. Remember that shocking last episode of the fifth season when they killed Jon Snow and we totally thought he was gonna stay dead? Well, let's list all the characters that would survive if he actually stayed dead.

MATCH (jon:Character {name: 'Jon Snow'})-[:KILLED]->(victim:Character)MATCH (jon)-[:VICTIM_IN]->(jon_death:Death)MATCH (jon)-[:KILLER_IN]->(victim_death:Death)<-[:VICTIM_IN]-(victim)WHERE victim_death.order > jon_death.orderRETURN DISTINCT victim.name AS victim, count(victim_death) AS kill_countORDER BY kill_count DESC;

11. If we want to see the above example in graph form, we have to add some modifications to the query, such as saving paths to variables that could be then written in RETURN.

MATCH (jon:Character {name: 'Jon Snow'})-[:KILLED]->(victim:Character)MATCH (jon)-[:VICTIM_IN]->(jon_death:Death)MATCH (jon)-[killed:KILLER_IN]->(victim_death:Death)<-[died:VICTIM_IN]-(victim)WHERE victim_death.order > jon_death.orderRETURN jon, killed, victim_death, died, victim;

12. Let's see how it looks like if we want to visualize all of Jon Snow kills with their locations.

MATCH (jon:Character {name: 'Jon Snow'})-[:KILLED]->(victim:Character)MATCH (jon)-[:KILLER_IN]->(death:Death)<-[victim_to_death:VICTIM_IN]-(victim)MATCH (death)-[death_to_location:HAPPENED_IN]->(location:Location)RETURN victim, victim_to_death, death, death_to_location, location

13. Who do you think was the biggest traitor in terms of killing in its own allegiance? Well, let's check it out!

MATCH (killer:Character)-[:KILLED]->(victim:Character)MATCH (killer)-[:LOYAL_TO]->(a:Allegiance)<-[:LOYAL_TO]-(victim)RETURN killer.name AS traitor, count(victim) AS kill_countORDER BY kill_count DESC;

14. To visualize the last example, we have to add paths between nodes in the result.

MATCH (killer:Character)-[killed:KILLED]->(victim:Character)MATCH (killer)-[:LOYAL_TO]->(allegiance:Allegiance)<-[loyal_to:LOYAL_TO]-(victim)RETURN killer, killed, victim, loyal_to, allegiance;

15. Memgraph supports graph algorithms as well. Let's use Dijkstra's shortest path algorithm to show the most gruesome path of kills. An example kill path is: Jon Snow killed 5 Lannister Soldiers and they killed 10 Stark soldiers with total kill_count of 15.

MATCH p = (:Character)-[:KILLED * wShortest (e,v | e.count) kill_count]->(:Character)RETURN nodes(p) AS kill_list, kill_countORDER BY kill_count DESCLIMIT 1;