By analyzing the repartition of gender, we clearly observe more male than female characters: there are precisely 4.4x more males than females.

Therefore Rick and Morty is far to respect gender equity... BUT! There is a "BUT"!

Click here to know more about gender equity

Besides we tagged as "genderless" the characters which are not gendered, like some aliens or robots. The only character tagged with "Both" genders is the duo Dipper and Mabel Mortys who represents a little Morty boy and a little Morty girl found at The Citadel of Ricks.

Attribute: Status

Bar plot of Status distribution:
241 Alive, 164 Dead characters and 109 "Unknown" status

Knowing the Rick and Morty cartoon show and all Rick's enemies, we would expect more deaths to be honest. Though there is still a part of mystery in the "Unknown" fates of characters, and we keep in mind that the "universe" is large.

Then, we could wonder: is the anti-hero we're all thinking about responsible of the dead people? Let's get the data speak... By tracking the "Category: Killed by Rick" in Wiki pages - yes, this category exists... We learn that 39% of deceased people were actually killed by Rick!

Attribute: Species

We note how various this universe is! Although, we mostly count Human beings - from Earths of different dimensions, and Aliens, in the Rick and Morty universe.

Bar plot of Species distribution
224 Humans, 143 Aliens, 45 Humanoids, 23 Mythological Creatures, 18 Robots, 14 Animals, 6 Cronenberg, 6 Poopybuttholes, 3 Bird Persons and <2 characters from each remaining species (including 1 Unknown)

Attribute: Origin

Origin is the attribute where we count the highest ratio of "Unknown" values... Indeed, neither the Wiki nor the external API we used can provide enough answers to this question. Most of the time, the origin of characters is not mentioned: they assume that knowing the associated species is sufficient, or the show isn't explicit enough on this characteristic. Therefore it was quite difficult to populate this attribute manually, in comparison with previous presented attributes.

Bar plot of Origin distribution
312 Unknown origin, 55 from Earth (Replacement Dimension), 25 from Earth (C-137), 8 from Post-Apocalyptic Earth, 7 from Nuptia 4, 6 from Earth (C-500A) and <5 from each remaining origin

Network Visualisation, with Force Atlas

Who's dead?

We must not fail to create network visualisations with Force Atlas spatialisation, in order to present the Rick and Morty characters.

First, we generated a visualisation with a node coloration based on status attribute.

We can distinguish some hubs of dead characters, like Parasites at the right side, or The Council of Ricks - destroyed in The Rickshank Rickdemption (S03E01).

Besides, we could use this data to predict the status of unknown characters.

Rick and Morty network status-based coloration: green for "Alive", redish pink for "Dead" and orange for "Unknown" status (click here to enjoy a better resolution)

Male, female or non-binary?

In the second network visualisation below, color matches gender attribute and node size is based on degree.

The two biggest nodes are Rick and Morty, the two main protagonists and adventurous explorers of our project. It is easy to notice the rest of the Smith family as well: the two biggest yellow nodes are Summer and Beth, and - of course, last but not least, Jerry (the stupid husband), as the third biggest purple node at right side, between his wife and his daughter.

Now, your task is to find where is the only green node - the duo Dipper and Mabel Mortys, we mentioned previously in the gender attribute section!

What about degree?

Degree stats & distributions

This is the IN and OUT degree distributions of our "Rick and Morty" network.

The two distributions are clearly different: extreme values (from 0 to 250) of IN-degrees are reached, especially the lowest values. In contrast, by analyzing the OUT degree distribution, degrees fluctuate from 0 to almost 40, which is quite lower than IN-degree.

Statistics for in and out degree

IN degree (top) and OUT degree (bottom) distributions of the Rick and Morty characters' network

In other words, it means first that a lot of characters are targetted by a few other characters, but some quite and very popular characters exist - those should be the main protagonists. In addition, the way characters target other ones is less unbalanced: we are at the vicnity of 3,7 = about 4 targetted characters per Wiki character page in average (std = 4). Besides, we note that the mean value in the same for in and out degree, but the variance is much greater in "in-degree" case, and the median is lower.

Comparison with a scale-free network

By viewing overall degree distribution, in particular by applying a log-scale, we recognize the manifestion of the scale-free property, with a power law shape, specific to the "Barabási-Albert" network - where nodes follow a preferiential attachment law (source: Network Science Book, by Barabási).

IN+OUT degree distribution, with linear (left) and logarithmic (right) scale

Thus, we can fit a power law to compute the degree exponent γ of both IN and OUT degree distributions, using powerlaw Python package.

Fitting a power law with in degree (left), and out degree (right), plotting with log scale

We computed the optimal xmin to minimize the error σ, which is low enough in both fits to validate the model.

Both degree exponents γ are between 2 and 3, so we are in "Ultra Small World" theorical regime: the groups of higly connected nodes (hubs) have a significant influence of connections in the network, hence they radically reduce the path length.

In addition, in-degree exponent is higher than out-degree exponent: this fact confirms our interpretation based on degree distributions.

Degree distribution of a scale-free network, following a power law (source: Chapter 4, Network Science Book)

Focus on our stars: the most connected characters

From the plots, it can be noticed that for the in and out degree, the first characters are the same: we find the Smith family in both distributions.

Then, for in-degree case, we find Evil Morty as first non-Smith C-137 character.

For ou-degree case, Memory Parasites appear as a highly connected node. Indeed they probably refer to all specific Parasites.

We also plotted the scatter plot of IN versus OUT degree, to study the reciprocity and answer the question: if a character is targetted a lot, is he/she targetting a lot of characters as well?

In general, as we seen in scale-free analysis with degree exponents for both IN and OUT degrees, we notice that characters are more likely to be targeted than target to other characters. However we detect an increase for both degrees at some point, especially for highest values of IN degree.

Thus, we tried to fit a linear regression on the last node. We studied the R-squared correlation coefficient to conclude that considering the last 9 nodes could be used in a linear model. We chose 9 nodes to fit the linear regression by computing the R-squared for different parts of the nodes, starting from the last ones and adding nodes progressively.

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A statistical overview of our Network

What a beautiful graph

Attributes

Attribute: Gender