The Cinephile’s Guide to The Galaxy

Jermain Kaminski and Michael Schober’s blog Movie Galaxies offers a quantitative analysis of popular films, drawing the social structure of each subject.

Every screenwriter uses a unique narrative structure in storytelling the same way the audience chooses a character they sympathize with while watching a movie. As previously seen in our X-Men article, fictional social structures share features with real-life ones.

In terms of network analysis, the density of a social structure has a strong impact on how a story unfolds. The definition of network density is the proportion of edges in a network relative to the total number of possible edges. The term – also used in sociology – shows how much an individual identifies with the group or people surrounding him/her, and is an indicator of social capital as well. But what does this have to do with movies?

Similar to sociology, narratology has strong emphasis on group membership, and the social and behavioral patterns that keep these companionships in tact. In a high density group most of the members are in constant contact with one another, the same way they are in a classic romantic flick. In contrast to this, a movie – like The Lord of the Rings Trilogy – operating with a larger cast has a lower level of density. This makes networks in the romantic genre smaller with larges dots and stronger edges. The story usually revolves around the main conflict between the female and male lead, that plays out directly, or through confessions to each parties closest friends.

The following picture represents an interesting aspect of the research:

The infographic shows how various directors deal with their characters as narrative time passes, and how their direction affects the density of the social network within the movie. Oliver Stone and Steven Spielberg obviously like to resolve their conflicts by the end of the movie, eliminating irrelevant story lines and characters, thereby increasing the density of the network. Quentin Tarantino and David Lynch however, like to confuse their viewer even more with adding some extra storylines to the movie around half-time, lowering density.

The site offers various movie social networks with films like 2001: A Space Odyssey, Twin Peaks, Pulp Fiction and many more. The seconds picture shows Paul Thomas Anderson’s epic, Magnolia. Those of you who have seen it know, that its storytelling uses the colliding storyline technique that was made popular by Thornton Wilder who first connected seemingly unrelated storylines in The Bridge Of San Luis Rey, and has since been mastered by directors like Akira Kurosawa or Alejandro González Iñárritu in Babel. It is instantly obvious, that the various storylines and the characters they operate form clusters connected by a single edge each.

For more movie networks, check out the site.

How Kevin Bacon Became the Center of the Universe

Six Degrees of Kevin Bacon is a well-known phenomenon and has saved boring parties on more than one occasion, but hence its popularity, its origins are less famous. The idea behind it however, has been swimming in interdisciplinary waters for decades.

Like many other forms of entertainment, Six Degrees of Kevin Bacon came to life in a dorm room of bored students, when they were watching a movie called The Air Up There, starring the actor back in 1994. What the movie lacked in entertainment, it made up for in inspiration: it made the students think about Bacon's silver screen appearances and co-starts. The point of the game is, to connect any actor to Bacon through mutual appearances with other actors within 6 steps. The number steps is the Bacon coefficient. The theory became huge in the nineties, its creators appeared on talk shows and had a book deal (there even was a boardgame). Along came the tech support, and an online version was made available called The Oracle of Bacon, that uses the ImDB database as a source, making 1.6 million actors potential players.

The average value of the Bacon coefficient is 2.99. Chuck Norris’s Bacon number is 2 (he starred in The Good Guys Always Wear Black with Anne Archer, who co-starred Bacon in Hero At Large in 1980). More surprisingly, Swedish movie legend Max von Sydow and Charlie Chaplin share the same number, for reasons other, than a silly Chuck Norris one-liner. It is only fair to warn you about the highly addictive qualities of the site, that might have reduced some countries GDPs through wasted work hours by slight percentages. The Oracle’s Hall Of Fame commemorates those, who have fought and conquered, by finding actors with 7 or higher coefficients, between 1996 and 2001. May they work in peace and return to their normal life now.

But what is behind the numbers? A not too complicated network that keeps America’s movie capital in motion, in which most actors are not more than 3 steps away. The brains behind the Oracle also calibrated the Connery coefficient with an average of 2.89, making the original 007 a more accurate center of the network.  Americas favourite pastime also has its own Oracle.

The underlying theory is called the Small World Network by Stanley Milgram, and was first used in network theory by László-Albert Barabási, who analysed – among others – the network of Hollywood. In networks like this (including the human brain), most dots only have a small amount of connections, while a few others have a lot, becoming the centers of the network, and making shortcuts in it. The theory has been successfully adapted into different areas, making seemingly chaotic pools of data (like the internet or the collapse of financial markets) comprehensible. Since its formation in 1967 the theory has not been tested widely, until now.

Not one, but two different academic research projects are headed in this direction. Firstly, sociologists at the University of Columbia are analysing demographic data to find information flow patterns and strategies in emails. Meanwhile the University of Ohio is working on  the social map of the internet, that will draw out connections between people of different status, and the flow of information within society, also scaling the size of different social networks.

Blood Ties, Comics and Morals

Our previous post proved, that popculture is a fertile ground for network analysis. We also found out that Peter Parker is not the only superhero with a net. Now it is time to take a look at previously undiscovered regions of network analysis.

The dedication of Marvel fans speaks for itself: the universe not only has its own Wikipedia, but an extensive character database too. A project called Marvel Chronology lists every appearance of every character that ever left ink on a panel – at least that is the idea. (Let the basic graphic outlay of the page not discourage you, the site itself claims that its sole purpose is to educate, not to please the eye.) This database got some scientists thinking: how different is this fictional world from ours? They compared the social structure of comics with real life patterns, and despite general statistical parallels, they discovered some odd things too. One of them was the fact that popular people in real life seek the company of other popular people, but not in comics. Spider-man and Captain America, two of the most popular Marvel vigilantes for example barely meet.

An American mathematician by the name Samuel Abersman took the idea to another level by examining a special kind of social bond, namely family ties. With a method usually used in population genetics, he calculated the inbreed coefficient (the higher the number, the higher the inbreeding) of characters. The real question was: how different are the two worlds? The subjects: the X-Men! The picture shows a visual aid and character network of mutants. At first glance the X-Men do not seem to be the inbreeding type, but a closer look reveals some black sheep. The deviants are Magneto’s twins Quicksilver and Scarlet Witch, who commit twincest on the pages of The Ultimates. Apart from that, the mutant inbreeding coefficients are rather low, outrunning some royal dynasties and a bunch of pharaohs in the race for moral heights.

Culture – now available in bottled shape

Fauna is a rather odd association when it comes to culture, but a bunch of network analysts might put that in the past tense.

Scientists of The University of Georgetown explored whether the tool use of some bottlenose dolphins in Shark Bay may be considered a cultural thing, when it comes to the forming of groups. The basis fo the research was that this behaviour is  both a cognitive action, and a grouping criteria. The research included 36 spongers (animals that use marinal sponges for hunting) and 96 traditionally hunting dolphins. It became instantly obvious, that homofilia (the tendency to associate with similar others) based on tool use is legit, while other factors include: maternal kinship, location and gender. This supports the theory, that tool use is a cultural phenomenon. Female spongers for example prefer spongers to non-spongers, the same way people prefer other people within the same subculture.

Bottlenose dolphins live in open communities, characterized by high fission-fusion dynamics where members maintain long-term preferential bonds, but associations are temporally and spatially variable across minutes, days and years. This tool use is unique among cetaceans, and was only observed among 55 dolphins in Shark Bay. The hunting tactic gets passed on vertically, since the hunting itself is done alone, while newborns learn the tricks and techniques when they accompany their mother.

The 22 year (!) research distinguished male, female, sponger and non-sponger dolphins and revolved around the social behaviour of the animals.

The first network in the picture shows the four big hubs formed by 105 dolphins, with the bigger points marking the spongers. The thickness of the links marks their strength, and draw out the network of spongers. The second network shows the whole 500 population, the nonspongers are coloured purple and light blue.

The whole article is available on the Nature homepage, feel free to check it out!

The Social Network of Masked Vigilantes

The past few years brought an exciting renaissance for comic geeks. Spiderman and Batman were reborn on the silver screen (not to mention the upcoming Superman movie in 2013) and the doors of the Marvel universe opened up not only for the fans of the stripes, but everyone else too. Former comic laymans got the chance to discover the world of Marvel heroes from the Avengers to Spiderman for themselves.

Regardless of their species – mutant, human or extraterrestrial - they are lovely and brave heroes, who have saved our lives and the world on more than one occasion.  The mass hysteria surrounding characters like Iron Man or Spiderman is not accidental; these heroes and stories are all excellently drawn and written, thereby shaping our image of the superhero and the everyday joe.

Many of us just began to discover the Universe of Marvel and DC.  The endless discussion about their quality and rank is pointless, since these stories and characters all are unique in their own way. Our analysis depicts the cooperation and fighting network of Marvel heroes. (We apologize to all DC lovers, but the Marvel database proved more accessible and user-friendly, and the authors of the article were biased due to successful marketing of the Avengers movie :)

This graph shows us what the significant marvel network looks like. Opponents, collaborators and central figures are shown, and connections between hubs representing teams are visible. An apology to Marvel fans is due as well: in order to downsize the Universe (including more than 25000 characters), we weighted connections by their frequency, and the final cut left those, who had more than 100 mutual appearances.

After the network was drawn, teams became instantly visible: the Avengers are in the centre coloured purple, the X-Men are red ones on the right, and green dots represent the Fantastic Four. Peter Parker aka. Spiderman did not get a unique color because he flies solo, but has a web of family, friends, romantic and hostile connections. The blue nodes stand for romantic involvements: i.e. Mary Jane Watson, future Mrs.Parker. The size of the nodes depends on the number of connections so the bigger the size of the name is, the more common stories the characters have.

At first sight we can establish that war veteran Captain America is excellently connected, with links to all the major teams. The X-Men are the biggest bunch, thanks to the next generation of New Mutants. Bridges – people in the network connecting teams and bigger groups – include Henry McCoy aka. Beast, and Thor who connects Asgardians and our realm.

Genius, billionaire, playboy, philanthropist Tony Stark and his red suit not only occupy the hearts female fans, but the center of the network as well. Their central role of various Avengers in the network collides with the sole purpose of the team: collecting the creme de la creme of the Marvel universe, and fighting mankind’s biggest enemies.

The double and triple connections represent relatives, loves and enemies who collaborated with others many times but are not included in the 3 main groups. Another curious aspect of the network is, that the individual characters are relatively old, compared to the teams: Marvel started publishing in 1939 under the name Timely Publications, while various teams were established in the sixties, adapting the various trends of comic history. The success they brought is undeniable. Their legacy: a magnificent Universe with more than 25 thousand characters speaking to fanatic and newcomer, old and young alike.

Mapping the web

A Russian coder by the name Ruslan Enikeev has created something extraordinary: The Map of the Internet. The map depicts a network based on traffic, with each dot representing a website, and each switch a link. The stronger the link (the more often people went from one site to the other) the closer they become on the map. The bi-dimensional scheme gathers information from over 350 million sites from 196 countries, and also works with a color scheme: sites relative to a country are painted the same, Russia is red, China is yellow, Japan is purple, and North-America is light blue. For the curious geeks physical and quantum-physical examples are drawn, and a mathematical analogy is also accessible to help with understanding the method. Different clusters are arranged according to their content. The network is based on data until the end of 2011. The purpose of the project was „an attempt to look into the hidden structure of the network, fathom its colossal scale, and examine that which is impossible to understand from the bare figures of statistics.”

The Hungarian segment shows that the most visited site up until 2011 included Google, the news portal Index, the blogging site blog.hu, origo, and Iwiw, the social networking engine that ruled the market before Facebook became popular. Other circles indicate frequent switches between university sites and funny blogs like sg or demotiváló, Imdb is the biggest foreign blog, Hungarians visited last year.

The project came to life with the help of Google Maps API and russian creative agency Positive Communications and claims to have no financial purpose but entertainment.            
The application operates under the following address.

Olympics, water polo and networks – a slightly biased pondering on chances

The whole world is wildly excited about this year’s olympic games, starting this Friday in London. Every country has its favourite sport, usually the one they are very good at: England has polo, Brazil football, and the United States cheers for just about everything, especially swimming. It is safe to assume, that water polo is the sport that makes the heart of Hungarian sportslovers throb the most. Earlier on, we have taken a look at analysis based on a network representing ball passing habits of football teams, but since the Hungarian team failed to make it to the European Championship, we were unable to conduct a more in-depth analysis. We did however prepare a small summary of the past 4 years olympic water polo results.  Maven7’s money is on Olympic Gold for Hungary, but our objectivity might have failed us on that one. On the other hand, we do have the last ten years results to back up our hunch.

The first picture shows all the games that took place at the last 4 olympics, with the dots representing various countries, green lines showing the winners, and the red ones the draws (i.e. France – Germany). The thicker the line, the more similar the outcomes between the teams were; in Hungary’s case, the strong connections to Greece, the Netherlands and Russia tell us, that we defeated them more than once. The size of the dots increases with the number of games won, whereas the size of the labels grows with the number of games played. The countries that seized to exist since the games are labelled white.


All in all 23 teams and 21 countries earned their quota to the Olympics in the past 12 years. The odd difference between those two numbers is due to changes of regime across Eastern Europe, that varied the names but not the line-up of the leading teams.  The most radical historical alterations happened in countries strongly tied to water polo: Serbia and Montenegro separated, as did Czechoslovakia, and finally the end of the CIS (Commonwealth of Independent States) united team in 1992, including 12 countries came.

The quite dense second picture shows the medal-winning countries. In the next picture the colouring was not calibrated according to Olyimpic regulations. In water polo an olympic gold scores 7, a silver 5, and a bronze 4 points. The yellower a dot is, the more points the country earned (Hungary has 21) in the past 4 Olympics, while the grey ones never made it to the podium.

With 3 gold medals, the Hungarian team is by far the most successful, but the competition is doing just  as well. The Italian, Spanish and Serbian team all did very well, according to the sizes of their dots. Their position in the network tells us, that they are serious challengers, and their experience makes them dangerous opponents, that have beaten us in the past. The biggest losers include  Germany, Greece and  Australia.

For a closer look at our important opponents we re-sized the network. The next picture only shows nations, that have multiple (at least two) connections, meaning that they have faced each other more often, with similar results. Our team has defeated Italy and Greece more than once, but has lost to Spain on multiple occasions. The Croatian team has to look out for the United States and Spain, while the latter one should fear Serbia. There is a definite possibility of meeting the coloured teams in the finals.

In order to have a better chance at pondering on odds, we drew this hierarchical network of the last Olympics. The dashed lines show the qualifying matches, with results on the edges. The green lines point to the winners, the red ones the draws. The dots are coloured according to medals; Hungary-gold, USA-silver and Serbia-bronze.  Hungary won all its matches, except for the draw against Montenegro. It is quite interesting, that we did not have a game against our top rival Serbia. Lets hope that this time the experts are right, and out defeat at the European Championship will not cast a shadow on this years performance, so we can once again stand on our well-reserved spot on top of the podium.

Enjoy the game!

The Team of Maven7