- So I'm the chair for the introduction.
So let's go to the topic directly.
Dynamic networks by definition is a network
whose structure is changing across time.
So at any time stop, the nodes and links
can be added or removed.
And this type of data is quite common
in many, many application domains
such as social network, email network,
and theoretical communication network.
And for this talk, let's just consider
academic collaboration network, co-authorship network.
So in co-authorship network, the nodes are authors,
and the links indicate a publications
between the two authors,
and this kind of network is also dynamic,
changing year by year.
So imagine that you have a dynamic network like this,
a co-authorship network,
and which could be fairly large.
You start at looking at the whole network
as the entire thing.
Another type of analysis is called egocentric analysis,
which essentially focus on a particular subnetwork
that has centered at a particular actor.
For example, could be me.
So you might call the network my ego network.
May contains my co-authors,
and may contains my co-authors' co-authors,
and may...it's called the ego,
that's why it's called ego network.
And co-authors, my co-authors are called authors.
As the whole network change across time,
so does the ego network.
So there exists many approach
realizing dynamic network in the literature.
The most basic approach is to using a small module
of no-link diagrams.
And there is an animation-based approach,
and you can also realize the dynamic network
as a series of matrices.
You can also use projection to reduce the whole network
into a single data point.
And there was a fantastic talk about,
which I didn't include here,
it's happening this morning,
about realizing dynamic networks using comics.
So those approaches, they have their own limitations,
such as those I list on the slides.
And most importantly, most of the work,
focusing on the whole network analysis,
tracking the changes of the entire network.
Whereas, egocentric analysis, the focus on the micro level,
more the analysis between the ego and specific authors.
So we can ask many questions about dynamic ego networks.
For example, in co-authorship network,
I might wonder, when does my co-author join or leave
or rejoin my network?
And how does he connected to me?
Is it direct connection or not?
And my co-authors may form different clusters, communities.
What is the temporal dynamics of that?
And what is the stability of my co-authorship network?
Is the turnover rate high or low?
To address those questions using realizations,
I got some inspiration from the subway maps,
or transit maps, like this.
And the particular map we're using here
is lines, stops and zones.
And can introduce in just a second.
So as I mentioned, in our ego lines realization,
we realize each other as a line,
just like the subway lines,
and you are traveling across time.
And the starting and ending of the line,
indicating the joining and leaving of the specific network.
And we place the focal author in the middle,
which is the ego,
and we also drew a packing algorithms
to lay out co-author lines around this focal author.
And we try to minimize the inline crossings,
and keep the lines straight if possible,
and make it look elegant.
If you don't pack those author lines together,
you may end up a larger realization such like this.
And certain authors may leave the network then rejoin,
so we indicate the missing years as a dash line.
And if the author leaves the network and never comes back,
we just terminate the lines.
It's like the subway line terminated.
And we represent connections using those kind of red dots.
It's based on matrix representations,
for example, MB and JF is kinda here.
We use it red dot here.
And those red dots in the subway map
is just like the stops on the line.
And we are using color to indicate the co-author clusters.
And the cluster is based on
how close the authors work together.
For example, the year in the middle.
And we are using this green contour to separate
the first-level co-authors and the second-level co-authors.
It's just like the zones in subway maps.
And now, I'm going to show you a demo of our system.
Alright, cool.
So this is a full interface of the equalized system.
And here, you see an overview of the entire system,
sorry, the entire network aggregated across time.
And here is a table view showing
each of the strikes of the ego network in the network.
And those histograms are indicating
certain variables changing course.
For example, the node number, links number and so on.
And the dataset I'm using here is co-authorship network,
based on the publications at CHI and actually WSEAS
conferences during the past 25 years.
And for this demo, let's explore,
while the CHI academy award winner this year,
Dr. John Stasco's ego network.
Let me enlarge the window a little bit.
Alright, so as you can see that John Stasco
has a very long publication history.
I need to zoom out to see all of his papers.
And also this is not all of them.
It's only CHI and inference papers.
It's not all of the papers.
And you can see that if you look at the size of the blocks,
there are bigger ones and smaller ones,
meaning that the size of the ego network,
his ego network, goes up and down.
And remember that this is his ego network that contains
both of his direct co-authors,
and those are his co-authors' co-authors.
So you might wonder what is the size trends
of his direct co-authors only.
So okay, using this kind of zoom tool,
I mentioned in the beginning,
so all his direct co-authors highlight in this gray zone.
And if you zoom out again, you can see that,
actually, his first-level ego network size
does not change that much.
It's stabler.
And it seems that this year, this big block here,
actually is 2003, is the first year
that John Stasco's ego network grows a lot, quite a bit.
And a lot of people are from, actually,
outside his immediate cycle, circle of this ego network,
and other people are from this red clusters,
and if you hover over, you can see there's black lines
indicating how he is connected to John Stasco.
For example, here, Dr. Nicholas Russel
is connected to Stasco through CP here.
And if you hover over others in the red cluster,
it seems that they all kind of connected
to Stasco through CP.
So who is it?
(intelligible speaker)
Bingo. (laughs)
So this doctor co-authoring prism...
so we observe a lot of traffic
going through Dr. Kathleen Preston
connecting to John Stasco in his ego network.
So it seems that she is a central hub.
So is that true?
We can verify it by changing the color mapping
from clusters to between this.
And you can see that John Stasco and Kathleen Preston
are two of the central hubs in this ego network.
While it's John Stasco's ego network,
of course, he is the central hub,
and in this particular year,
Kathleen Preston is also the central hub.
Let's go back to clusters, and keep browsing,
and 2007 is also an interesting year
because John Stasco's first-level ego network
grows quite a lot if you look at those lines
in the red zones.
And you might wonder, what are those people working on?
You can hold particular thoughts,
so the title of the paper is shown below.
And because many people may work on
more than one papers in a year,
so it's a width in the graph, actually.
So you can show the address by using this.
And you can observe a couple black dots on there,
meaning that those people working on
more than one papers in that year.
And you can hover over on it,
and those are all about the famous jigsaw system,
a fundamental contribution to our community.
And we keep browsing, we find 2008 also interesting detail.
The size of dots change a lot compared to 2007,
but there are many clusters, many colors.
You might wonder what do those clusters mean?
You can sort those lines by their clusters.
So you can see there is a community here,
a community here, and if you browse those others,
you see Dr. Bochan Lee and Dr. Daniel Fischer.
Those are people from MSR.
And because the clustering is based on
how close people work together in a year, so it might,
in some sense, reflect the organizational information.
So I suspect that those purple clusters
are the MSR conditions.
Another insight you may have is
in John Stasco's ego network, there is many lines,
and they are like very short.
Meaning that the turnover rate of the network is high.
And how high is it?
We can adjust slider so here, I'm only showing
people that have more than three years of relationship
with John Stasco.
So you see that this dynamic, this we're addition side
will decrease dramatically,
meaning that the turnover rate is high.
And you can also review this gray zone tool,
and we observe that most of the people
are Stasco's direct co-authors.
That makes us because people who have direct relationships
may work for longer time.
And there's one exception, interesting fact here,
is Dr. Bochan Lee.
So she works with just Stasco in 2008,
and then leaves the network in 2009,
and then rejoined the network in 2010
as a secondary co-author.
And then she connected to John Stasco
through Dr. Ji Chung Liu here,
and I believe that corresponds with the fact that
Ji Chung Liu internship at MSR around that time.
Alright, so that's the demo.
Let's go back the slides.
Let's go back the slides again.
Alright, thank God.
So to evaluate ego lines, we did a controlled-user study,
and we designed 13 tasks including temporal analysis
and topological analysis.
And we compared ego lines with two other techniques,
using the dynamic co-authorship network as the testing data.
And the two other techniques were compared to,
one is the small mod codes of the no-link diagrams,
and with a basic approach, the baseline.
And the other technique we compared to is called,
we call it no links.
It used the ego lines as the layout algorithm,
but it shows the connection as links.
And a detailed discussion is in the paper,
and now I'm going to show you
the main takeaway of our study.
So overall, ego lines performed the best.
For temporal analysis, ego lines was on par with no links
in terms of time, but more accurate.
And small module was the worst.
And for topological analysis, ego lines performed the best
for both time and accuracy.
Small module was faster than no link,
but they have similar accuracy.
And there's one exception is that for funding bridges task,
small module was the best,
and ego lines was the worst.
That might be cause the bridge concept
in matrix representation is
a little bit difficult to comprehend.
In addition, we also did a domain expert evaluation
with an expert.
So we interviewed sessions.
And the expert is a measurement school professor
who interest in organizational networks analysis.
And during the interviews, he explored email communications
from a company.
We developed a comprehensive scenarios from those studies,
and for details, I encourage you to read the paper.
And in the future, we like to address
some of the limitations of the current ego line system.
So the first of all, we want to do a more effect overview
because right now it's basic,
and we want to reduce the visual clutters.
And we also want to handle larger ego networks
by incorporating some kind of
like a modest-scale aggregation typing,
such as you can expand or collapse the lines and so on.
Now we'll like also to do experiments other than size
and other applications, and most importantly,
to figure out what can we do to make the bridge funding
tasks more effective with ego lines.
And with that, I like to thank all my co-authors,
and thanks for your attention,
and take any questions you have.
(applause)
- [Man] (unintelligible) I messed up.
(laughter)
- Okay, I decided to mess it up.
So I'm Drasco from Columbia University.
I have a bit of philosophical question here.
Why are you saying that you're using the method
for us of subway?
The question here is, let's say,
key two characteristics of a subway is that
it's covering a two-D space,
which means the vertical has also some kind of a meaning.
And also, another thing is, what is a stop,
and where can I see switching between stops here, right?
Was it necessary to say the subway metaphor,
or it could be just lines and dots
and not even mentioning that?
Like, what's the reason behind it?
- That's a great question.
So for sure, this is not a subway map.
We are leveraging some of the real elements from the subway,
and they're inspired by the subway
and all public transit maps.
And the particular elements we are using here is
zones, lines and stops.
And the white dots, it looks similar to subway,
but it's not exactly a subway line,
and you can imagine that if you're in order,
you traveling across time,
and you encounter several stops, those white dots,
and that is the collaboration
happening with other co-authors, so in that case,
actually it represents some kind of events.
And another interesting insight during our studies,
we found using the subway metaphor is easier to explain
the realization to novice users,
and they don't know.
They generally see matrices as fairly complicated, for now,
it's user who don't understand realization enough,
not in computer signs, not in maths.
They don't know these matrices.
But if we talk in a different way
using this kind of subway map for
and this kind of language,
they get the realization correctly.
That is why we use it.
- [Man] I think you got one of the dots wrong in 2001.
I noticed that it wasn't right.
- I downloaded the data set from your website.
(laughter)
(applause)
- [Man] I actually did have a question, though.
The vertical positioning of particular people,
what's the algorithm, and is how sensitive is the view
to changes there?
- That's a great question.
So we, it's a three-D algorithm, and so we first,
we sort those lines by their starting times,
and those are the lengths,
and then we place the ego in the middle,
and then we balance the lines above and below,
based on their total lengths above and below the ego.
And so we are using this kind of three-D algorithm
to place those lines by that particular order,
and that's inspired by the old papers using the inside out
here is take to place those lines.
And I would say that not often the approach
because it does introduce some kind of line crossings
in some time, and especially for you using,
introducing zones, and first-level,
they need to be inter-center.
So it's a limitation.
(applause)
- Thank you.