IMDb Graph - Documentation

Introduction TODO

Understanding the data

We are taking the data from the official IMDB dataset: https://datasets.imdbws.com/

In particolar we're interest in 3 files

• title.basics.tsv
• title.principals.tsv
• name.basics.tsv

Let's have a closer look to this 3 files:

title.basics.tsv.gz

Contains the following information for titles:

• tconst (string) - alphanumeric unique identifier of the title
• titleType (string) – the type/format of the title (e.g. movie, short, tvseries, tvepisode, video, etc)
• primaryTitle (string) – the more popular title / the title used by the filmmakers on promotional materials at the point of release
• originalTitle (string) - original title, in the original language
• isAdult (boolean) - 0: non-adult title; 1: adult title
• startYear (YYYY) – represents the release year of a title. In the case of TV Series, it is the series start year
• endYear (YYYY) – TV Series end year. ‘\N’ for all other title types
• runtimeMinutes – primary runtime of the title, in minutes
• genres (string array) – includes up to three genres associated with the title

title.principals.tsv.gz

Contains the principal cast/crew for titles

• tconst (string) - alphanumeric unique identifier of the title
• ordering (integer) – a number to uniquely identify rows for a given titleId
• nconst (string) - alphanumeric unique identifier of the name/person
• category (string) - the category of job that person was in
• job (string) - the specific job title if applicable, else '\N'
• characters (string) - the name of the character played if applicable, else '\N'

name.basics.tsv.gz

Contains the following information for names:

• nconst (string) - alphanumeric unique identifier of the name/person
• primaryName (string)– name by which the person is most often credited
• birthYear – in YYYY format
• deathYear – in YYYY format if applicable, else '\N'
• primaryProfession (array of strings)– the top-3 professions of the person
• knownForTitles (array of tconsts) – titles the person is known for

Filtering

All This section refers to what's inside the file filtro.py

Now that we have downloaded all the files from the dataset, we have to filter them and modify them in order to easly work with them.

name.basics.tsv

For this file we only need the following columns

• nconst
• primaryTitle
• primaryProfession

Since all the actors starts with the string nm0 we can remove it to clean the output. Furthermore a lot of actors/actresses do more than one job (director etc..), to avoid excluding important actors we consider all the one that have the string actor/actress in their profession. In this way, both someone who is classified as actor or as actor, director are taken into consideration

df_attori = pd.read_csv(
  'name.basics.tsv.gz', sep='\t', compression='gzip',
  usecols=['nconst', 'primaryName', 'primaryProfession'],
  dtype={'primaryName': 'U', 'primaryProfession': 'U'},
  converters={'nconst': lambda x: int(x.lstrip("nm0"))})
df_attori.query('primaryProfession.str.contains("actor") or primaryProfession.str.contains("actress")', inplace=True)

Then we can generate the final filtered file Attori.txt that has only two columns: nconst and primaryName

---

title.basics.tsv.gz

For this file we only need the following columns

• tconst
• primaryTitle
• isAdult
• titleType

Since all the movies starts with the string t0 we can remove it to clean the output. In this case, we also want to remove all the movies for adults.

There are a lot of junk categories considered in IMDb, we are considering all the non adult movies in this whitelist

• movie
• tvSeries
• tvMovie
• tvMiniSeries

Why this in particolar? Benefits on the computational cost. There are (really) a lot of single episodes listed in IMDb: to remove them without loosing the most important relations, we only consider the category tvSeries. This category list a TV-Series as a single element, not divided in multiple episodes. In this way we will loose some of the relations with minor actors that may appears in just a few episodes. But we will have preserved the relations between the protagonist of the show. It's not much, but it's an honest work

print("Filtering films...")
df_film = pd.read_csv(
  'title.basics.tsv.gz', sep='\t', compression='gzip',
  usecols=['tconst', 'primaryTitle', 'isAdult', 'titleType'],
  dtype={'primaryTitle': 'U', 'titleType': 'U'},
  converters={'tconst': lambda x: int(x.lstrip("t0")), 'isAdult': lambda x: x != "0"}) #
df_film.query('not isAdult and titleType in ["movie", "tvSeries", "tvMovie", "tvMiniSeries"]',
              inplace=True)
filtered_tconsts = df_film["tconst"].to_list()

Then we can generate the final filtered file FilmFiltrati.txt that has only two columns: nconst and primaryName

---

title.principals.tsv

For this file we only need the following columns

• tconst
• nconst
• category

As before, we clean the output removing unnecessary strings. Then we create an array on unique actor ids (nconst) and an array of how may times they appear (counts). This will give us the number of movies they appear in. And here it comes the core of this filtering. We define at the start of the algorithm a constant MIN_MOVIES. This integer is the minimum number of movies that an actor has to have done in his carrier to be considered in this graph. The reason to do that it's purely computational. If I have to consider all actors the time for the code to compile is the year(s)'s order, that's not good. We are making an approximation: if an actor has less then a reasonable (42, as an example) number of movies made in his carrier, there is an high probability that he/she has an important role in our graph during the computation of the centralities.

print("Filtering relations...")
df_relazioni = pd.read_csv(
  'title.principals.tsv.gz', sep='\t', compression='gzip',
  usecols=['tconst', 'nconst','category'],
  dtype={'category': 'U'},
  converters={'nconst': lambda x: int(x.lstrip("nm0")), 'tconst': lambda x: int(x.lstrip("t0"))})
df_relazioni.query('(category == "actor" or category == "actress") and tconst in @filtered_tconsts', inplace=True)
nconsts, counts = np.unique(df_relazioni["nconst"].to_numpy(), return_counts=True)
filtered_nconsts = nconsts[counts>=MIN_MOVIES]
df_relazioni.query("nconst in @filtered_nconsts", inplace=True)

Notice that we are only selecting actors and actresses that have at least a relation.

print("Re-filtering actors...")
nconsts_with_relations = df_relazioni["nconst"].unique()
df_attori.query("nconst in @nconsts_with_relations", inplace=True)

print("Re-filtering films...")
tconsts_with_relations = df_relazioni["tconst"].unique()
df_film.query("tconst in @tconsts_with_relations", inplace=True)

At the end, we can finally generate the file Relazioni.txt containing the columns tconst and nconst

Understanding the code

Now that we have understood the python code, let's start with the core of the algorithm, written in C++

Data structures to work with

In this case we are working with tow simple struct for the classes Film and Actor

struct Film {
    string name;
    vector<int> actor_indicies;
};

struct Actor {
    string name;
    vector<int> film_indices;
};

Then we need two dictionaries build like this

map<int, Actor> A; // Dictionary {actor_id (key): Actor (value)}
map<int, Film> F; // Dictionary {film_id (key): Film (value)}

The comments explain everything needed

Data Read

This section refers to the function DataRead()

void DataRead()
{
    ifstream actors("data/Attori.txt");
    ifstream movies("data/FilmFiltrati.txt");

    string s,t;
    const string space /* the final frontier */ = "\t";
    for (int i = 1; getline(actors,s); i++)
    {
        if (s.empty())
            continue;
        try {
            Actor TmpObj;
            int id = stoi(s.substr(0, s.find(space)));
            TmpObj.name = s.substr(s.find(space)+1);
            A[id] = TmpObj; // Matlab/Python notation, works with C++17
            if (id > MAX_ACTOR_ID)
                MAX_ACTOR_ID = id;
        } catch (...) {
            cout << "Could not read the line " << i << " of Actors file" << endl;
        }
    }

    for (int i = 1; getline(movies,t); i++)
    {
        if (t.empty())
            continue;

        try{
            Film TmpObj;
            int id = stoi(t.substr(0, t.find(space)));
            TmpObj.name = t.substr(t.find(space)+1);
            F[id] = TmpObj;
        } catch (...) {
            cout << "Could not read the line " << i << " of Film file" << endl;
        }
    }
}

We are considering the files Attori.txt and FilmFiltrati.txt, we don't need the relations one for now. Once that we have read this two files, we loop on each one brutally filling the two dictionaries created before. If a line is empty, we skip it.

Building the Graph

This section refers to the function BuildGraph()

void BuildGraph()
{
    ifstream relations("data/Relazioni.txt");
    string s;
    const string space = "\t";

    for (int i=1; getline(relations,s); i++){
        if (s.empty())
            continue;
        try {
            int id_film = stoi(s.substr(0, s.find(space)));
            int id_attore = stoi(s.substr(s.find(space)+1));
            if (A.count(id_attore) && F.count(id_film)) { // Exclude movies and actors filtered
                A[id_attore].film_indices.push_back(id_film);
                F[id_film].actor_indicies.push_back(id_attore);
            }
        } catch (...) {
            cout << "Could not read the line " << i << " of Releations file" << endl;
        }
    }
}

In this function, we only ose the file Relazioni.txt. As done before, we loop on all the elements of this file, creating

• id_film: index key of each movie
• id_attore: index key of each actor

Then we exclude the add with .push_back this two integers at the end of the vectors of their respective dictionaries. If a line is empty, we skip it.

---

Closeness Centrality

That's where I tried to experiment a little bit. The original idea to optimize the algorithm was to take a uniformly random subset of actors. This method has a problem: no matter how smart you take this random subset, you are going to exclude some important actors. And I would never want to exclude Ewan McGregor from something!

So I found this paper and I decided that this where the way to go

The problem

Given a connected graph G = (V, E), the closeness centrality of a vertex v is defined as

 \frac{n-1}{\displaystyle \sum_{\omega \in V} d(v,w)} 

The idea behind this definition is that a central node should be very efficient in spreading information to all other nodes: for this reason, a node is central if the average number of links needed to reach another node is small.

This measure is widely used in the analysis of real-world complex networks, and the problem of selecting the k most central vertices has been deeply analysed in the last decade. However, this problem is computationally not easy, especially for large networks.

This paper proposes a new algorithm that here is implemented to compute the most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie.

---

In order to compute the k vertices with largest closeness, the textbook algorithm computes c(v) for each v and returns the k largest found values. The main bottleneck of this approach is the computation of d(v, w) for each pair of vertices v and w (that is, solving the All Pairs Shortest Paths or APSP problem). This can be done in two ways: either by using fast matrix multiplication, in time O(n^{2.373} \log n) [Zwick 2002; Williams 2012], or by performing a breadth-first search (in short, BFS) from each vertex v \in V , in time O(mn), where n = |V| and m = |E|. Usually, the BFS approach is preferred because the other approach contains big constants hidden in the O notation, and because real-world networks are usually sparse, that is, m is not much bigger than n$$. However, also this approach is too time-consuming if the input graph is very big

Preliminaries

In a connected graph, the farness of a node v in a graph G = (V,E) is

 f(v) = \frac{1}{n-1} \displaystyle \sum_{\omega \in V} d(v,w)

and the closeness centrality of v is 1/f(v) . In the disconnected case, the most natural generalization would be

 f(v) = \frac{1}{r(v)-1}\displaystyle \sum_{\omega \in R(v)} d(v,w) 

and c(v)=1/f(v), where R(v) is the set of vertices reachable from v, and r(v) = |R(v)|.

But there is a problem: if v has only one neighbor w at distance 1, and w has out-degree 0, then v becomes very central according to this measure, even if v is intuitively peripheral. For this reason, we consider the following generalization, which is quite established in the literature [Lin 1976; Wasserman and Faust 1994; Boldi and Vigna 2013; 2014; Olsen et al. 2014]:

 f(v) = \frac{n-1}{(r(v)-1)^2}\displaystyle \sum_{\omega \in R(v)} d(v,w) \qquad \qquad c(v)= \frac{1}{f(v)} 

If a vertex v has (out)degree 0, the previous fraction becomes \frac{0}{0} : in this case, the closeness of v is set to 0

The algorithm

In this section, we describe our new approach for computing the k nodes with maximum closeness (equivalently, the k nodes with minimum farness, where the farness f(v) of a vertex is 1/c(v) as defined before.

If we have more than one node with the same score, we output all nodes having a centrality bigger than or equal to the centrality of the k-th node. The basic idea is to keep track of a lower bound on the farness of each node, and to skip the analysis of a vertex v if this lower bound implies that v is not in the top k.

More formally, let us assume that we know the farness of some vertices v_1, ... , v_l and a lower bound L(w) on the farness of any other vertex w. Furthermore, assume that there are k vertices among v_1,...,v_l verifying

f(v_i) > L(w) \quad \forall ~ w \in V \setminus \{v_1, ..., v_l\}

and hence f(w) \leq L(w) < f (w) \forall w \in V \setminus \{v_1, ..., v_l\}. Then, we can safely skip the exact computation of $f (w) for all remaining nodes w, because the k vertices with smallest farness are among v_1,...,v_l.

Let's write the Algorithm in pseudo-code, but keep in mind that we will modify it a little bit during the real code.

 Input : A graph G = (V, E)
    Output: Top k nodes with highest closeness and their closeness values c(v)
        global L, Q ← computeBounds(G);
        global Top ← [ ];
        global Farn;

        for v ∈ V do Farn[v] = +∞;
        while Q is not empty do
            v ← Q.extractMin();
            if |Top| ≥ k and L[v] > Farn[Top[k]] then return Top;
            Farn[v] ← updateBounds(v); // This function might also modify L
            add v to Top, and sort Top according to Farn;
            update Q according to the new bounds;

• We use a list TOP containing all “analysed” vertices v_1 , ... , v_l in increasing order of farness
• We also need a priority queue Q containing all vertices “not analysed, yet”, in increasing order of lower bound L (this way, the head of Q always has the smallest value of L among all vertices in Q).
• At the beginning, using the function computeBounds(), we compute a first bound L(v) for each vertex v, and we fill the queue Q according to this bound.
• Then, at each step, we extract the first element v of Q: if L(v) is smaller than the k-th biggest farness computed until now (that is, the farness of the k-th vertex in variable Top), we can safely stop, because for each x \in Q, f (x) \leq L(x) \leq L(v) < f (Top[k]), and x is not in the top k.
• Otherwise, we run the function updateBounds(v), which performs a BFS from v, returns the farness of v, and improves the bounds L of all other vertices. Finally, we insert v into Top in the right position, and we update Q if the lower bounds have changed.

The crucial point of the algorithm is the definition of the lower bounds, that is, the definition of the functions computeBounds and updateBounds. Let's define them in a conservative way (due to the fact that I only have a laptop and 16GB of RAM)

• computeBounds: The conservative strategy needs time O(n): it simply sets L(v) = 0 for each v, and it fills Q by inserting nodes in decreasing order of degree (the idea is that vertices with high degree have small farness, and they should be analysed as early as possible, so that the values in TOP are correct as soon as possible). Note that the vertices can be sorted in time O(n) using counting sort.

• updateBounds: the conservative strategy does not improve L, and it cuts the BFS as soon as it is sure that the farness of w is smaller than the k-th biggest farness found until now, that is, Farn[Top[k]]. If the BFS is cut, the function returns +\infty, otherwise, at the end of the BFS we have computed the farness of v, and we can return it. The running time of this procedure is O(m) in the worst case, but it can be much better in practice. It remains to define how the procedure can be sure that the farness of v is at least x: to this purpose, during the BFS, we update a lower bound on the farness of v. The idea behind this bound is that, if we have already visited all nodes up to distance d, we can upper bound the closeness centrality of v by setting distance d + 1 to a number of vertices equal to the number of edges “leaving” level d, and distance d + 2 to all the remaining vertices.

What we are changing in this code is that since L=0 is never updated, we do not need to definite it. We will just loop over each vertex, in the order the map prefers. We do not need to define Q either, as we will loop over each vertex anyway, and the order does not matter.