Who is the most important person in the film industry?
By: Steve Hedden
Tags: Data Analysis, Network Analysis, Data Science, Movies, Data Visualization

William Shakespeare (left), Robert De Niro (middle), and Miles Long (right). Headshots from IMDB.com

Who is the most important person in the film industry?

Network analysis using IMDB data

Kevin Bacon once said that he “had worked with everybody in Hollywood or someone who’s worked with them,” which led to the parlor game “Six degrees of Kevin Bacon” along with various parodies, ads, boardgames, talk show appearances, and even the charity ‘SixDegrees.org’. The game consists of trying to connect an actor to Kevin Bacon through shared films with other actors, within six connections. For example, Nicolas Cage was in Adaptation with Meryl Streep who was in The River Wild with Kevin Bacon (2 degrees separated meaning Nicolas Cage has a “Bacon Number” of two). There are also various online resources, like The Oracle of Bacon, to calculate an actor’s “Bacon Number”.

Nicolas Cage is two degrees separated from Kevin Bacon, meaning Nicolas Cage has a “Bacon Number” of 2. Nodes (or vertices) in blue and links (or edges) in orange — explained in more detail below.

But is Kevin Bacon the ‘most connected’ or “most important” person in the film industry? To answer this question, I downloaded the entire IMDB database. This is a rich dataset (like over 3.6 million people in over 5.1 million titles dating back to 1894 rich) and once you start “extracting the network” from this data it gets huge. By “extracting the network” data I just mean that we are interested in not just the people and the movies but also all of their connections. To make this analysis easier, I used only movies made after 1960 with at least 4,171 IMDB ratings (the mean) and an overall rating of above 1.0. Even still, once we extract the network, we get over 12,000 movies, over 50,000 people, and half a million unique connections!

A network consists of nodes (or vertices) and links (or edges). In the case of the IMDB network, we can represent people using nodes and collaboration in the same film as a link. In network analysis, there are many different ways of measuring the “importance” of a node in a network, called measurements of “centrality”. I put quotes around the word “importance” here because that is a subjective term and there are different ways a node can be important to a network. That is why there are different centrality measures.

Probably the simplest measure of centrality is the total number of connections a node has. This is called “degree centrality” since the number of links a node has is referred to as the “degree” of the node. For example, Kevin Bacon has been in 34 films with 298 collaborators, meaning his “degree” in this network is 298. The more connections a person has, the higher the degree centrality. A person with high degree centrality is a ‘hub’ in a network. Degree centrality is intuitive and informative, but does not account for secondary connections and can miss the importance of satellite clusters in the network.

Eigenvector centrality is a measure that describes the position of a node in a network as a function of its direct connections which, in turn, are calculated as functions of their direct connections, and so on. Eigenvector centrality identifies ‘hubs’ but also people with connections to hubs. This measure thus accounts for nodes with a relatively few but important number of connections. PageRank (after Larry Page), the algorithm Google uses to rank websites, is a variant of eigenvector centrality — making the assumption that “important” websites will be referenced by other “important” websites. Eigenvector centrality accounts for more than just direct connections but can also miss satellite clusters.

Betweenness centrality is calculated by finding the shortest path between each set of two nodes. For any two nodes, there exists a shortest path between them. The more shortest paths travel through a node, the higher its betweenness centrality. The nodes that act as ‘bridges’ or ‘brokers’ between many nodes or clusters of nodes have higher ‘betweenness centrality’. For example, in an electricity network, where nodes are electrical substations, and links are flows of electricity, a substation that lies on the shortest path between many other substations would have a lot of electricity flowing through it and thus, a higher betweenness centrality. Betweenness centrality can be used to evaluate which node is most important for extracting information from a network and clearly highlights the nodes that act as bridges to other clusters, but is less useful in identifying important nodes within the densely packed central cluster.

Closeness centrality is defined as the sum of a node’s distances to all other nodes and can be used to evaluate which node is most important for disseminating information. In other words, a node with high closeness centrality is more ‘embedded’ in the network than other nodes.

The image below shows a subset of connected nodes from the network. The size of the nodes corresponds to their centralities for the four different measures, and the colors correspond to their different “communities” (explained more below).

Different centrality measures: degree (top left) eigenvector (top right) betweenness (bottom left) and closeness (bottom right)

As discussed above, each of these measures have strengths, weaknesses, and uses. Degree centrality (top left) favors nodes with many connections, so there are some large nodes in the main cluster as well as the red satellite cluster. Eigenvector centrality (top right) concentrates on the red satellite cluster rather than the central cluster. Betweenness centrality (bottom left) highlights nodes that act as bridges between clusters — notice how all the nodes that connect the central cluster to the satellite cluster are relatively large. In the closeness centrality graph (bottom right) notice how most nodes are larger compared to the other centrality measures. This implies that the network itself, or at least that central cluster, is very dense i.e. all nodes are ‘close’.

Let’s apply these centrality measures to the entire network of 50,000 people. The ten people with the highest degree centrality are:

Robert De Niro (actor) Nicolas Cage (actor) Jerry Goldsmith (composer) Samuel L. Jackson (actor) James Newton Howard (composer) Scott Rudin (producer) Bruce Willis (actor) Mark Isham (composer) Michael Caine (actor) James Horner (composer)

This means that these men have worked with the most people during their careers. While they may not all be household names, this list really isn’t that surprising. All of these men have made many movies with many people for many years. Robert De Niro has been a principal in 82 movies with 613 unique collaborators.¹ Jerry Goldsmith passed away in 2004 but was also a principal in 82 films. The ever-prolific Nick Cage has made 73 films with 602 and people, five of them last year alone.

But making a lot of movies doesn’t necessarily make you the most influential person. Last year, the most prolific person in “film” was the adult director and cinematographer Miles Long, who was principally involved in 41 movies with 265 total collaborators.² Robert De Niro only made one film with nine collaborators. No offense to Mr. Long but I would find it hard to argue that he was a more central player in the film industry in 2018 than Robert De Niro. Eigenvector centrality doesn’t just add all the connections but also accounts for how well connected each of those people are. If we apply this measure of centrality to our IMDB network we get the following list of important people:

Scott Rudin (producer) Robert De Niro (actor) James Newton Howard (composer) Brian Grazer (producer) Samuel L. Jackson (actor) Bruce Willis (actor) Hans Zimmer (composer) Tom Hanks (actor) Carter Burwell (composer) Jerry Goldsmith (composer)

While many of the names remain, the changes in the list highlight some important differences between degree centrality and eigenvector centrality. Brian Grazer, Hans Zimmer, Tom Hanks, and Carter Burwell have been added to the list while Nicolas Cage, Mark Isham, Michael Caine, and James Horner have dropped off, meaning that the newly added people have fewer connections but that the “importance” of these connections is higher than those that were removed. For example, Hans Zimmer has been principally involved in “only” 48 films with 360 total collaborators but these connections are more “connected” than those 602 of Nick Cage.

Degree and eigenvector centrality can tell us a lot about a network but they can miss some important insights. If we calculate the betweenness centrality of the network we get nearly an entirely different list of top ten:

William Shakespeare (writer) A.R. Rahman (composer) Nicolas Cage (actor) Irrfan Khan (actor) Robert De Niro (actor) Gökhan Tiryaki (cinematographer) Ennio Morricone (composer) Jerry Goldsmith (composer) Samuel L. Jackson (actor) Willem Dafoe (actor)

Betweenness centrality is a function of the number of “shortest paths” that go through a node. The more shortest paths travel through a node, the higher its betweenness centrality. This is how we can have a very different list of “most important” people — these are people who serve as bridges from the central cluster to satellite clusters in the network. To illustrate this, I ran a community detection algorithm to identify significant clusters of individuals. A “community” within a network is essentially just a group of nodes that are more densely connected to each other than with the rest of the network.

The image below shows some prominent nodes according to betweenness. The size of each node corresponds to their betweenness and the color corresponds to their community. Here we see why A.R. Rahman and Irrfan Khan are ranked so high — they are the bridge from Hollywood to Indian film. Likewise, Gökhan Tiryaki is the bridge from Hollywood to the Turkish film industry, Jackie Chan is the bridge to the Chinese film industry, Penélope Cruz is the bridge to Spanish film, and Juliette Binoche and Gérard Depardieu connect Hollywood to French cinema. Perhaps most interestingly, William Shakespeare is the most influential node in terms of betweenness centrality. This is because his work has been around for the entire lifespan of film and because almost all film industries around the world have made Shakespearian films. To explore the connections yourself see this interactive network visual.

So, who is the “most important” person in Hollywood? It depends what you mean by “important”. In the context of the Six Degrees game, Robert De Niro is probably your best bet unless you’re playing with someone very familiar with film industries outside the US, then try William Shakespeare. And if you were wondering, Kevin Bacon ranks 100th in terms of degree centrality, 87th for eigenvector centrality, and 167th for betweenness centrality.

Here’s all of this analysis as a Jupyter Notebook.

Thank you Jess Rettig, Dave Bohl, and Ion Ho for your assistance!

[1] IMDB data only lists about seven to ten “principals” per movie. This usually means that the writer, director, producer, composer, and a few actors are listed. So, anyone in a movie who is not on this list is not accounted for in this analysis. For example, Johnny Depp is not listed in Chocolat — Juliette Binoche, Carrie-Anne Moss, Alfred Molina, and Dame Judie Dench were top billed. This also explains why there are four composers in the top ten list — the composer is almost always listed as one of the principals.

[2] While I filtered out the “adult” category of the IMDB data for this, Mr. Long’s work is categorized as “movies” and so was included, though they were all dropped when I filtered out movies without over 4,171 ratings.