{"id":11038,"date":"2022-04-26T09:00:00","date_gmt":"2022-04-26T13:00:00","guid":{"rendered":"https:\/\/dvsnightingstg.wpenginepowered.com\/?p=11038"},"modified":"2022-04-25T08:52:16","modified_gmt":"2022-04-25T12:52:16","slug":"building-and-visualizing-a-social-network-through-the-vikings-example","status":"publish","type":"post","link":"https:\/\/nightingaledvs.com\/building-and-visualizing-a-social-network-through-the-vikings-example\/","title":{"rendered":"Building and Visualizing a Social Network through the Vikings\u2019 Example"},"content":{"rendered":"\n

As an avid fan of the Vikings<\/em><\/a>, I was extremely excited about the new spin-off on Netflix titled Vikings: Valhalla<\/em><\/a>. Simultaneously, my network-and-data-scientist self was just as curious about how the original series and the new one, set a century later, will relate to each other. Thus, I decided to build a network map of the realm of the Vikings<\/em> shows to examine the series in a quantified, objective way, following my previous explorations of the Game of Thrones<\/em><\/a> or The Witcher<\/em><\/a>.<\/p>\n\n\n\n

The Vikings\u2019<\/em> data<\/h2>\n\n\n\n

As in any data science project, data collection was one of the first major steps I took. To construct the network of the Vikings\u2019<\/em> characters, I needed two things: the list of characters (that will be the network nodes), and information about their relatedness (serving as network links). While using the TV series directly could certainly work well, here I simplified the problem and relied on the Vikings<\/em><\/a> <\/a>Wiki<\/a>. In particular, I started with the character-listing<\/a> subpage shown in Figure 1. This site showcases the full list of characters in a comfortable, well-structured html format that I collected and parsed in Python with urllib and bs4. My scripts can be found in this GitHub repo<\/a>.<\/p>\n\n\n\n

\"\"
Figure 1. Vikings<\/em> Wiki character listing<\/a>.<\/figcaption><\/figure><\/div>\n\n\n\n

After collecting the full cast of the Vikings<\/em>, including the name and Vikings<\/em> Wiki identifier (profile URL ending) of about three hundred characters, I started scraping their individual biographical profiles <\/strong>(corresponding code in the GitHub repo). As the example of the famous and infamous Ragnar Lothbrok<\/a> shows in Figure 2, these profiles have a whole bunch of hyperlinks pointing to other characters – basically, network links. For instance, Ragnar is linked to his first wife, Lagertha the shield-maiden, his children, Gyda and Bjorn, and even his treacherous brother, Rollo.<\/p>\n\n\n\n

\"\"
Figure 2. Vikings<\/em> Wiki character profile\u200a\u2014\u200athe example of Ragnar Lothbrok<\/a>.<\/figcaption><\/figure><\/div>\n\n\n\n

Building the network<\/h2>\n\n\n\n

After having all the biographies in hand, I started building the network. In this network, every character is represented by a node, and they are linked (for the case of simplicity, in an undirected way) if there is at least one reference connecting their profiles, while the strength of their connection is proportional to the number of these references. Following a few simple intermediate steps (see the GitHub repo again) and feeding these references into the NetworkX<\/a> library, I arrived at a graph with 221 nodes connected by 1,104 edges. While NetworkX and Matplotlib are great and handy to quickly visualize networks directly from the data in an iPython Notebook (see Figure 3 for the raw visualization), it is usually a lot easier to design and fine-tune mode detailed network visualizations with Gephi<\/a>.<\/p>\n\n\n\n

\"\"
Figure 3. The raw network visualization of the Vikings with 221 nodes and 1,104 edges, drawn with Matplotlib and NetworkX.<\/figcaption><\/figure><\/div>\n\n\n\n

So to obtain a more digestible network visualization, let\u2019s turn to Gephi<\/a>. Gephi is open-source interactive software that can use graph data in various formats (.gexf, .graphml, .csv, etc.) as input data that one can also easily generate directly from NetworkX in Python. Furthermore, Gephi contains several\u00a0 visualization features like node sizing, coloring and opacity tuning, different implemented network layout algorithms like Force Atlas or Fruchterman-Reingold, and advanced tools for computing different network statistics such as centralities or modularity. The raw network opened in Gephi is shown in Figure 4.<\/p>\n\n\n\n

\"\"
Figure 4. The Vikings<\/em> network imported into Gephi from a gexf file built with NetworkX.<\/figcaption><\/figure><\/div>\n\n\n\n

With the raw network file open in Gephi, I started working out the final design, first by setting the node size to be proportional to the node\u2019s degree (number of connections they have) and applying my favorite Force Atlas 2 layout. <\/strong>This layout algorithm places the network nodes on a plane as if gravity-like physical forces were pushing them apart and pulling them together, providing an organic-looking image revealing the network\u2019s core structure. Next, I used a modularity-finding algorithm<\/a> that clusters nodes together based on connectivity patterns, resulting in clusters of nodes that have much stronger connections towards each other than towards the rest of the network. I colored the nodes according to these network communities. Then, to get a clear network visualization, I filtered out those connections that had a weight lower than two. In other words, I discarded those cross-references between characters that are not signaling frequent interactions. Following these steps, I arrived at Figure 5 showing the backbone network of the Vikings<\/em> social map <\/strong>with 149 nodes and 399 links.<\/p>\n\n\n\n

\"\"
Figure 5. The social map of the Vikings<\/em>. In this network, each character is represented by a node, and two characters are connected if their Vikings<\/em> Wiki sites reference each other. The more frequently they co-reference each other, the stronger the link, while the node size is scaled to the number of connections each character has. Additionally, nodes are colored based on network communities\u200a\u2014\u200adensely interconnected subsets of the network. This filtered network has 149 nodes and 399 edges.<\/figcaption><\/figure><\/div>\n\n\n\n

Reading the network<\/h2>\n\n\n\n

Let\u2019s understand a few stories the network visualization tells us. <\/strong>First, I checked which characters are the most pronounced hubs\u200a\u2014\u200awho has the highest number of connections? This simple measure says that the top 10 most connected characters are Ragnar, Bjorn, Lagertha, Harald, Ubbe, Ivar, Rollo, Hvitserk, Floki, and Torvi, in this order. From this list, only two characters are not members of Ragnar\u2019s family: Floki, who despite not being blood-related, probably still counts as family, and Harald, who connects the old Vikings<\/em> series to Valhalla.<\/p>\n\n\n\n

Diving further into the network\u2019s degree distribution, it turns out that Ragnar has 85 connections, followed by Bjorn\u2019s 71 links, and to make the family whole again\u200a, \u200aLagertha ranks third with 66 edges. Additionally, almost 30 percent of the links connect to at least one of the top 10 characters, while 160 characters have less than 10, and 48 characters have less than three connections. A zoomed-in version of Figure 5 with these hubs is shown in Figure 6.<\/p>\n\n\n\n

\"\"
Figure 6. A zoomed-in view of Figure 5.<\/figcaption><\/figure><\/div>\n\n\n\n

When looking at the strengths of the connections, the strongest one turns out to be between the (second) most famous Viking couple\u200a\u2014\u200aBjorn and Torvi. They are followed by the link of Harald Finehair and his great-grandson Harald Sigurdsson, directly connecting the original Vikings<\/em> series to Vikings: Valhalla<\/em>. The ten strongest links are typically dominated by Ragnar\u2019s sons (central blue cluster), and surprisingly, a Valhalla-connection in the top as well. This link connects the two main characters of Valhalla, who are also siblings, Freydis Eriksdotter and Leif Eriksson. Interestingly, the strength of Ragnar\u2019s strongest link is just about 35 percent of the link between Bjorn and Torvi, implying that Ragnar\u2019s connectivity patterns were rather oriented towards diversification than the strength of relationships, which seems logical to a true explorer.<\/p>\n\n\n\n

Finally, it is also worth taking a look at the network communities, which are subgraphs of the network within which it is more likely for two nodes to be connected than having links outside of the community. Extracting network communities is a widely researched area, thus I simply relied on a widely accepted tool<\/a>. As Figures 5 and 6 illustrate the communities via coloring, one can quickly read how the early seasons centered by the two brothers, Ragnar and Rollo are on the left, while the main body of the episodes starred by Ragnar\u2019s sons is in the middle. On the left side of the figures, it\u2019s also noticeable how the new series builds up its clusters, being connected with the strongest link to the original series via the bridge of the Haralds. Additionally, it is also visible that there are English scenes on the upper part of the network\u200a\u2014\u200ashowing connections between the original and the spin-off shows once again.<\/p>\n\n\n\n

Concluding remarks<\/h2>\n\n\n\n

To wrap up, in this short piece I illustrate how we can build the character network of the TV show Vikings<\/em> using their fan wiki site and simple tricks in Python. Additionally, I show the different steps of visualizing, reading, and interpreting this network.\u00a0<\/p>\n\n\n\n

Of course, as with most projects at this scale, it leaves many questions open and many directions for further improvements, such as more accurate character-mention (and name-) matching, differentiation between the types of mentions (e.g., bio text, family relations), and the quantification of connections based on the mentioning texts\u2019 sentiment.<\/p>\n\n\n\n

Finally, the corresponding Python source code and Gephi files are available in this GitHub repository: https:\/\/github.com\/milanjanosov\/VikingsNetwork<\/a>.<\/p>\n

Categories<\/span>How To<\/a> Topics in Dataviz<\/a> Use Tools<\/a><\/div>
Tags<\/div>Charts<\/a>Data Visualization<\/a>How To<\/a>social network mapping<\/a><\/div>","protected":false},"excerpt":{"rendered":"

As an avid fan of the Vikings, I was extremely excited about the new spin-off on Netflix titled Vikings: Valhalla. 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I studied and researched at the E\u00f6tv\u00f6s Lor\u00e1nd University and the Central European University in Budapest, at the Barab\u00e1si Lab in Boston, and the Bell Labs in Cambridge. 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This happened in July 2017, right before the seventh season of the iconic TV show was aired.\u2026","rel":"","context":"In "Data Science"","block_context":{"text":"Data Science","link":"https:\/\/nightingaledvs.com\/.\/topics-in-dv\/data-science\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/09\/Figure3.png?fit=1200%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/09\/Figure3.png?fit=1200%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/09\/Figure3.png?fit=1200%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/09\/Figure3.png?fit=1200%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/09\/Figure3.png?fit=1200%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":9836,"url":"https:\/\/nightingaledvs.com\/a-network-map-of-the-witcher\/","url_meta":{"origin":11038,"position":1},"title":"A Network Map of The Witcher","author":"Mil\u00e1n Janosov","date":"December 23, 2021","format":false,"excerpt":"The Witcher, a fantasy novel series by Andrzej Sapkowski, has inspired multiple computer games and was even launched on Netflix in 2019, with the latest season airing in December 2021. Fictional storylines such as The Witcher, can be well summarized and simplified by network science, extracting the backbone of a\u2026","rel":"","context":"In "How To"","block_context":{"text":"How To","link":"https:\/\/nightingaledvs.com\/.\/how-to\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2021\/12\/fig3.png?fit=1200%2C1182&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2021\/12\/fig3.png?fit=1200%2C1182&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2021\/12\/fig3.png?fit=1200%2C1182&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2021\/12\/fig3.png?fit=1200%2C1182&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2021\/12\/fig3.png?fit=1200%2C1182&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":10538,"url":"https:\/\/nightingaledvs.com\/is-there-a-happy-ending-for-the-peaky-blinders\/","url_meta":{"origin":11038,"position":2},"title":"Is There a Happy Ending for the Peaky Blinders?","author":"Mil\u00e1n Janosov","date":"March 1, 2022","format":false,"excerpt":"Peaky Blinders is an award-winning historical drama series produced by BBC Studios. The show is set between the two world wars and is centered around Birmingham, England, starring a notorious gangster family, the Shelbys. After five seasons and nearly half a million ratings in the Internet Movie Database, by now,\u2026","rel":"","context":"In "Data Science"","block_context":{"text":"Data Science","link":"https:\/\/nightingaledvs.com\/.\/topics-in-dv\/data-science\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/02\/peaky_network_v3.png?fit=1200%2C1200&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/02\/peaky_network_v3.png?fit=1200%2C1200&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/02\/peaky_network_v3.png?fit=1200%2C1200&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/02\/peaky_network_v3.png?fit=1200%2C1200&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/nightingaledvs.com\/wp-content\/uploads\/2022\/02\/peaky_network_v3.png?fit=1200%2C1200&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":12985,"url":"https:\/\/nightingaledvs.com\/a-tale-of-books-and-bias\/","url_meta":{"origin":11038,"position":3},"title":"A Tale of Books and Bias","author":"Celia L\u00f3pez","date":"September 20, 2022","format":false,"excerpt":"Books were one of our first methods of data storytelling-beyond speaking or paintings. Humans turned data into words, and now we have begun to turn those words back into data. We typically analyze books and their texts for their content or for the effect they have on us. But what\u2026","rel":"","context":"In "Data Science"","block_context":{"text":"Data Science","link":"https:\/\/nightingaledvs.com\/.\/topics-in-dv\/data-science\/"},"img":{"alt_text":"Tablet with bookshelves and students searching and reading information. Digital learning, online database, content storing and searching, ebooks concept. 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