Welcome to Metaphorical Visualization
A paper about mapping from data to anything
like cat images or stars in the sky
Now, the idea for the paper comes from
seeing visualization as a form of a metaphor from data to visuals
This works well for visualization because
we as humans tacitly understand visual primitives
I don't have to explain to you how a large circle is larger than a small circle
And can we push this further?
Can we map to something beyond the visual?
We think that we can
For example, consider these three points X, Y, Z
And what if I told you that the similarity between these points
is like the similarity between words "dog", "house" and "chimney"?
Now already in your head you probably have some idea of what that might be
If you're anything like me, "house" and "chimney" sound quite similar
like chimney is a part of a house
"Dog" and a "house" ... maybe somewhat
because a dog could live in a house, or it could be a "doghouse"
"Dog" and "chimney" ...
probably not so much, unless there's a dog Santa or something
So we could represent this with this little diagram here
using traditional visualization
But we didn't have to do this
because you already feel and perceive the similarities between the words
So, if we have a similarity function for them
we could actually compute this automatically
And this is exactly what we do,
so we call this "distance-based metaphors"
Given some points and a distance function between them
and concept points like images or words
also with a distance function or a similarity function
Then we can map from data to concepts, so, each point gets mapped
such that the pairwise distances,
for example, between these points [are] preserved
So far away points stay far away from each other and vice versa
Now, where would we get the simlarity functions for words
and cats, and things like that?
Well, it's 2022, so we use ML
For example, for words we can use word2vec, which is pretty well-known
and just map our data to this word embedding
using its similarity function to represent our data
Let's look at an example
Here we have VIS authors, so people who have
published at this conference and a word embedding
Now we project both
Here we have the author embedding and here we have the word embedding
And we project them just to compare to traditional visualization,
it is not strictly required.
These lines represent a part of our metaphor that we have computed
For example, Maurice Green becomes the word "user"
We're using author aliases here to
avoid copyright issues and things like that,
but there is a slight connection that you might be able to figure out
So, coming back to Maurice Green, he becomes "user"
and, for example, someone similar to him is Hernandez becomes "measurement"
Makes sense, "user" and "measurement" from technical literature
But there are also more interesting cases, for example
Krista Murphey, who appears to be far away from him in the original visualization
actually gets mapped to a "mouse"
"User", "mouse", like a computer mouse. Actually quite strongly related.
But even better, "mouse" is also related to "insect", in a biology kind of way
And you see, we get these cases, and actually quite often
where a word has multiple meanings
[that] it uses to connect to different parts of this space
Kind of exploiting the high dimensionality of a word space compared to a 2D plot
Now an application for this could be, say, we encode your reasearch interests
and you print that single word, or several words on your badge
and then you can go to a social event and just
kind of, have a fun way of approaching people to
figure out if you have something in common or not
Let's look at a different example -- CHI authors to cats
First thing we need to do again, we need to train a cat embedding
Now, cats are very easily embedded into stuff, so [it's] quite straightforward
and we get out this nice space
where simlar cats are mapped to nearby points
so we get low distances between them and we can use this for our metaphor
As a result, we get out these clusters
Like, for example, this white-gray, black-white kind of a cluster
And if we check, all the authors there are working in visualization or visual analytics
Similarly, we easily find a cluster of black cats
corresponding to mobile and ubiquitous computing
And there is a kind of a small outlier cluster
with orange cats in cages
that correspond to psychology and sociology
which is an appropriate metaphor
Of course, if you're not okay with mapping to cats
and you don't want to use a cat images as your avatar,
we can also style your avatar based on your research interests
For this, we do the SIGGRAPH authors to styles metaphor
We train a style embedding, I'm [going to] skip the ML details of that
And we get out also a nice space
where nearby images have similar styles
And what we do then, is that we map authors to these style donor images
and we can transfer the artistic style back onto [your] bio photo
For example, these two pairs have similar research
and therefore, they get mapped to a similar style donor
and get a similar style in their photograph,
which you can immediately see from looking at the pictures
And as a counter-example, consider these dissimilar pairs
It's also pretty straightforward to notice that
these authors have very [...] different styles
and therefore, their research interests might also be quite different
Now, we have many more examples of these metaphors, which I have to skip here
so please check out the paper.
And now I'm going to jump to the best example, in my opinion
"movies to stars" metaphor
Here we have taken the top movies and mapped them to stars in the night sky
Getting out this nice large infographic
But this is not just a distance-based metaphor, this is a hybrid approach.
So, we preserve distance like before.
Similar movies get mapped to nearby stars in the sky
But we also make sure that movies with a high rating
are mapped to the brightest stars,
so the best movies shine in the sky, sort of
As a result, we get out these nice clusters
For example, kind of movies constellations,
here we have Booetes, which becomes this anime constellation
The best, the highest-rated movies here is "Spirited Away",
which gets mapped to the brightest star
We get out this, sort of, Tarantino-ish constellation
with "Pulp Fiction", "Reservoir Dogs", but also "Snatch"
And my favorite, Corona Borealis gets these Pixar and Disney movies
and specifically "Lion King" gets mapped to Alphecca
Now, Corona Borealis is known as the Northern Crown
and Alphecca is called [the] jewel of the Northern Crown
So "Lion King" gets the jewel of the crown, which is kind of cool
Honestly, every time I look a the sky now
and I see Corona Borealis, I immediately think "Lion King"
like this is burned into my brain
Maybe it's a nice property of this metaphor also
that you kind of connect with the data in this way
So quickly summarizing the differences in our opinion
between metaphors and traditional vis
We think metaphors are more flexible
You can map to anything, you can map to cats, to dogs, cars, stars
dinosaurs, pyramids, aliens
really anything
Metaphors are also quite engaging
like, it's more fun to look at cats than at barcharts
And intuitive, in the sense that it's easy to spot similar cats
but maybe it's not intuitive to read a PCA plot or something like this
However, of course, they sacrifice accuracy
because figuring out exactly how similar two cats are
is more difficult than reading a barchart
And they also don't provide a visual overview,
the kind of aggregation you might get from a scatterplot
Therefore, we consider their applciations to be strongest in
Personal Visualization, informal contexts, where you're engaging with ...
[Where] engagement is more important that the accuracy, maybe
Or you are conveying data to a non-scientific audience
there it could be really nice.
And most importantly, do you have any ideas?
The goal of this paper is to put out this concept,
this crazy concept to the community
And maybe you have some cool application idea that we don't see
Because we are, sort of, still in search of that killer-app to show it off.
The paper and the supplemental materials have way, way, way more
So please check that out too, and thank you for listening