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Teamspector is a complex networks framework for experimenting with collaborative data. Currently it includes:

  • An IMDb data extraction and pre-processing tools,
  • An EDA notebook for exploring IMDb data,
  • Scripts to build a social network from IMDb data, and to extract each movie's social and success metrics.

Getting Started

Teamspector needs MongoDB, and should be installed in its own virtual environment. For installation instructions, see After installing Teamspector, download the IMDb data sources, extract the data to a local MongoDB collection, run the pre-processing rules:

$ download imdbws
$ extract imdbws
$ preprocess imdbws

There is a smaller experiment that runs faster, just to test things. It has the id 0. The main experiment has id 1. To run an experiment:

$ experiment imdbws <experiment id>


The IMDb dataset is provided by Amazon: This website describes in detail the data that's made available. All movies are considered, except for:

- Movies without year of release,
- Not feature-length, cinema productions (`titleType != movie`),
- Adult movies (`isAdult = True`).


During pre-processing, movies' number of votes (numVotes) is converted into a logarithm log_votes, as it has an exponential distribution.

The average ratings of movies are normalized into nrating using a Bayesian estimate, as there are wild differences in the sample sizes taken into account to produce the average. We suppose the Bayesian estimate can be trusted only for movies that received five thousands of votes or more, hence nrating is only defined for movies with numVotes ≥ 5000.

To calculate movie's success metrics, we check how well a movie did in terms of log_votes and nratings, compared to other movies produced in the same year. The percentile of a movie's log_votes and nratings within movies produced in the same year is computed as ypct_votes and ypct_rating.

To produce our final movie score metric, ypct_votes and ypct_rating are summed. The movie's percentile of this sum is computed into ypct. For this percentile, we only consider movies produced in the same year. In other words, if a movie has ypct = 0.99, this means the movie is considered better than 99% of the movies produced in the year the movie came out.

Finally, a binary success metric, top100, is defined to capture if the movie was one of the best 100 movies produced in the year as measured by ypct. It's only after 1985 that we have more than 100 movies each year that have a valid nratings value, hence the top100 metric is only defined for movies produced this year or afterwards.


The goal of this experiment is to test the effect of social network structure in human work. The experiment infers the social network graph from a group of workers. Workers are nodes. Nodes who previously worked together are connected via an edge. Edges are unordered, and their strength is proportional to the number of previous works jointly conducted.

Edges and their nodes are removed from the graph after being inactive for eight years. Only productions in which workers are part of the graph's giant component are considered.

For each piece of work produced, we measure aspects of the social structure from workers responsible for the piece of work, and success metrics related to the piece of work. Afterwards, tests are conducted to measure the effect of social structure on work success.

In the context of movie production, works are actors, producers, writers and directors. Pieces of work are movies, their success metrics are ypct and top100. The uses the IMDb dataset to build the graph of workers. For each movie produced, network metrics from workers and success metrics from movies are stored in a separated table.


The experiment runs in a loop, where each year is analyzed at a time. IMDb only provides the year of release of movies, forcing us to consider movies produced in a given year are all produced and released simultaneously.

We begin with an empty list of network graphs L.

Before each iteration of the loop, each graph in L is inspected and nodes that haven't produced any work for over 8 years are removed along with their edges. If this causes any of the graphs to become disconnected, the disconnected graphs are added to L, while the original graph is removed from L

Then, we look into the productions released in the year. For each production, we find the graphs in L which contain any of the production's workers. Workers not present in the graph are added to the graph as new nodes. Edges between workers are added, or strengthened in case they already exist. If we have selected more than one graph from L, the now connected graphs are joined into a single graph, which is added to L, and the original graphs which were joined are removed from L.

Time Frame

Experiment metrics are only collected starting from 1985, as success metrics might not be reliable prior to this year. The experiment runs from 1985 to 2012. The reason 2012 was chosen instead of a more recent year is that we don't know how long it takes after a movie is released for ypct and top100 to stabilize. We assume five years is a safe pick.

Team Structure

For a large portion of movies, IMDb provides a list of producers, directors, writers and actors. For some famous movies, more elements from the production team may be available, such as editors, composers, and cinematographers.

Due to computational constraints, the experiment considers only producers, directors and writers. Adding actors, the graphs become exceedingly big for centrality network metrics to be feasibly computed.

Network Metrics

For each production team, we collect social metrics related to each worker in the team individually (Ego metrics), to the pairs of workers in the team (Pair metrics) and to the team as a whole (Team metrics).

Ego Metrics

Each production will have as many ego metrics as the number of people in its team. Hence, the maximum, minimum, median and standard deviation from the metrics are calculated.

Team Metrics

Via vertex contraction, all nodes that participated in the production being studied are temporarily transformed in a single node. Then, all ego metrics are recalculated for the contracted node representing the team. Besides that, another metric is taken:

  • Team size: the number of nodes in the production team.

Pair Metrics

  • Shared Collaborators: the number of nodes connected to both nodes of the pair,
  • Neighbour Overlap: the shared collaborators number, divided by the number of nodes connected to either one of the nodes of the pair, the proportion of friends which are connected to both nodes,
  • Past experience: the weight of the edge connecting the two nodes,

Production teams with more than two members will have many pairs. Hence, the same aggregate statistics used for the Ego metrics are also used for the Pair metrics.


If you like this project and want to participate, there's a lot of ways you can help. Check out for more info.


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