So there he is.
Hey everyone.
Welcome, Sinan.
Thank you.
Thanks very much for the invitation.
It's a pleasure to be here, although only remotely.
I wish I could be there in person.
And today, just what I wanted to do is to give you guys a bit of beginning to lay out
some fundamentals on the table, so to speak, so that as you go though the day, hear other
speakers, talk about applied problems and networks, or how they're applying technology
to solve different tricky puzzles in the world, that you have a basic understanding of how
the structure of networks evolved, and evolves, and how that relates to things like creativity,
innovation, access to information, productivity, and so on.
And I wanted to really just start with the basic building blocks of networks and work
you up to a few pieces of knowledge that might help you in those conversations.
So as you see on my first slide here, this is the entire Facebook network as of December
2010.
A very very complex structure now approaching two billion people that are connected in day
to day interaction with one another.
And it spans the entire globe as is obvious from this slide.
But on the next slide, what you'll see is that these networks have very specific and
complex structures.
And if you think of the links in a networks as guiding the flow of resources, of social
capital, of information, of ideas, of trends and so on, then the way that these resources,
and information, and creativity flow through human society is guided in large part by networks.
By who we know.
By who we talk to.
Who we collaborate with.
And how we structure those collaborations.
Let me start by giving you a few examples of this network structure so that we can get
sort of a mental image of the different types of structures that might exist.
This slide here is a slide showing the structure of the interdisciplinary collaboration network
at the Santa Fe Institute.
And the nodes are colored.
These are people.
And each of them have a different color for their theoretical area.
So there's one for the structure of RNA.
One for Statistical Physics.
One for Mathematical Ecology.
One for Agent Based Models.
And you notice immediately that they are sort of grouping into groups amongst one another
that are talking more to one another than to others outside of the group.In the next
slide you'll see a high school friendship network where the nodes are colored by which
year they are in high school.
So junior, senior, freshman, sophomore, junior and senior year.
And you again start to notice a little bit the separation of the different types of people
in terms of how they're cooperating.
Now that's a high school friendship network, but in the next slide what you'll see is a
high school dating network.
And if we click forward twice you'll see a couple of notes that I've made about this
network.
The first one is that the structure of the dating network and the friendship network
are very very different obviously.
This one has almost a tree like structure.
Whereas if you think about the friendship network, it was much more complete in the
sense that many more people were friends with each other than there are people who are dating
one another.
And if you see this tree like network, it comes from the fact that people tend to, in
general, be monogamous.
To be dating one person at a time.
It's also clear from the colors that these typically go boy, girl, boy, girl.
But if you look closely there are a number of places where that's not true, reflecting
same sex couples.
Some people have many more dating partners than others.
And you see that variation, but you immediately begin to notice how social structure can tell
us so much about how social groups are organized, how information flows, and how influence might
spread through these networks.
On the next slide, you can see that we can actually map these information flows.
If you advance one more you'll see a note about how we map these information flows,
and this is the phone call traffic network at a global media firm.
Just one small bit of it where the thickness of the lines are more frequent communication
between two employees, and the direction indicates who's calling whom.
And when we analyze things like call log networks we can see exactly how information is flowing
through a network.
In the next slide you'll see another example of this, which is an email network at an executive
recruiting firm.
There you see, if you advance once more, that location matters a great deal.
This company is organized into a central office in the middle, with multiple satellite offices
with just a few representatives in each office.
And the communication that happens within this company is very much guided by geography
in addition to the information needs and the different types of people that work there.
Now let me take into the building blocks of network structure and how human networks evolved
into what we know today.
To really understand that we begin with what Granovetter called the Forbidden Triangle.
And if you advance to see the text there, what you'll see is that the basic theory of
Granovetter's forbidden triangle is that if A has a strong tie to B, and if A also has
a strong tie to C, then that dotted line between B and C indicates that it's very likely that
B and C have at least a weak tie.
And why is this the case?
The reason for this is because if A is good friends with B and good friends with C, B
and C are very likely to be in the same social circles.
To bump into one another because they are hanging out with B.
If A likes B, then probably have very similar preferences to B. And if A likes C, they probably
have similar preferences to B. So B and C are going to have similar interests, similar
preferences.
They're going to have a reason to want to be together.
Another good reason they're likely to be friends is because if they're not friends,
that's going to create a lot of social discord between A being friends with B, and A being
friends with C. Each of them will say, "Why are you friends with C?
I don't like C." And so on so forth.
So all of these social factors make it highly likely that B and C will eventually become
friends.
And if you advance one more, you'll see what the term that describes this, which is triadic
closure.
Human networks are replete with triadic closure.
As A is friends with B, and B is friends with C. B and C become friends, and this creates,
what's seen on this slide, clustering in networks.
What you see in human social networks are clumps, or clusters of people that are densely
connected within, and if you advance one more, they are loosely connected across by what
are called weak bridging ties.
And this is true of every single social network of human beings that has ever been studies.
And it is just sort of a structure of human networks that is ubiquitous.
In the next slide what you see is an example of how these networks look when you consider
the Forbidden Triangle and this tendency to cluster.
You see cliques that are bunches of human beings connected together deeply within, with
weak ties connecting them across.
This a co-authorship graph of academics publishing together.
In the next slide what you see is [00:08:30] biotech collaborations amongst firms and university
labs at the Massachusets Institute of Technology, the Children's Medical Center Corporation,
and so on and again you see this clustering pattern become evident in even these types
of collaborations.
In the next slide, what you see is that even true of our individual networks.
This is Owen Mundy's Facebook graph.
He's a sociologist who wrote a paper, and he put his [00:09:00] own Facebook graph in
the paper, in the study, and what you see there is distinct communities of people that
he knows, for instance his University of California at San Diego community where he did his graduate
study.
Or his Florida State community where he teaches.
Or his family community, his high school community, and his undergraduate community at Indiana
University.
And all of us have these types of networks that are clustered.
And that has tremendous implications for how we collaborate and how we share
information.
In the next slide you see the result of this clustering.
So what human beings are characterized by in their human social networks are high clustering,
as you see in the top part of the graph.
But very low path links as you see in the bottom part of the graph.
And this was shown by Duncan Watts and Steve Strogatz in 1998 as a fundamental property
of human social networks.
Very high clustering, but low path links.
And that's made possible by these cliques of people that are connected together by these
weak bridging ties that keeps the path links between any two people on the human social
network short, but keeps the level of clustering very high.
In the next slide, you'll see another example of this which is a Norwegian network of interlocking
board director members of different companies.
Again you see this clustering.
These are clusters that are connected by weak bridging ties.
And this structure that is born of the Forbidden Triad creates what we now know as six degrees
of separation because of those short path links we can get to anyone in the world by
six degrees of separation.
It also creates what's known as the strength of weak ties, because those weak bridging
ties are what hold the human social network together, and what enable information
flow and collaboration flow and idea flow across that network to different cliques in
the network.
In the next slide you see another consequence of this high clustering and low path links.
If you advance one more you'll see what you would expect is a bell curve of a degree distribution.
A degree distribution is just a distribution of the number of links everyone has in the
network.
You might expect that this is bell curves, that most nodes have an average number.
There are some highly connected nodes, and some very lowly connected nodes.
But most have an average number.
But given high clustering and low path links, what we actually have is on the right hand
side.
We have power log degree of distributions.
There are very many nodes with only a few links, and there are only a few hubs with
a large number of links.
And those are the very popular people.
The people who know everyone and so forth.
And the next slide you'll again see a typology of individual networks which shows you that
our friends cluster into these densely connected clusters within, and sparsely connected clusters
without.
There's a college network, a high school network, a family network, or a neighborhood network.
And in the next slide you begin to see some of the consequences of this.
If you advance a couple of times and see the words, you'll see that in addition to clustering,
what we know about human social networks is that the clusters separate people
with similar knowledge and information.
So I've color coded a hypothetical network here in different colors to indicate areas
of expertise.
And typically what you see is that people with the same area of expertise cluster together,
and they're separated by these weak bridging ties to other clusters who have different
areas of expertise.
What does this mean?
It means that the value of information that's shared in a network comes [00:13:00] from
its uneven distribution across the local network neighborhoods.
Each local network neighborhood gets deep in its subject matter, but doesn't know much
about another neighborhood.
So connection to these diverse neighborhoods give you access to novel pools of information
and this novel information is valuable due to its local scarcity.
If I know somebody in another neighborhood that knows the solution to my problem, but
I'm the only one in my cluster [00:13:30] that knows it, that information is very valuable
to my cluster and I can help my team by bringing that over from the other cluster.
This gives actors with scarce, novel information brokerage opportunities and ability to create
innovation in a network.
In the next slide what you see is an example of these brokerage opportunities.
Here again if you advance a couple more you see these clusters that have different expertise,
and [00:14:00] the red node in the middle is a broker that connects these clusters and
facilitates the flow of information to solve locally intractable problems with knowledge
coming from the outside that represents out of the box thinking.
In the next slide you'll see just an example which shows here's a person the left with
four friends.
But all of those friends know each other.
And here's a person on the right again with the same number of friends, four friends,
but none of them knows one another.
So the person on the right has contacts to many different pools of information, the person
on the left is swimming in the same pool of information as all the people that they know.
In the next slide you see the consequences of the tensions between these two structures.
The highly cohesive strong tie network on the left gives you more information, but more
redundant information.
It's more trusted relationships.
It's more resilient, more tight knit.
But also more homogenous thinking and may create group think.
The network on the right, you get less information because those are weak ties, but you get more
novel information per unit of information that you receive.
You have arm's lengths relationships.
These are brittle, they could be broken by too much time not talking to one another over
time.
They're loosely coordinated, but they're also very diverse and independent which gives you
a bit of a wisdom of crowds effect.
And if you go the last slide, I'll just describe how dynamics play into this situation.
If I'm the person who has the four friends, and I can create a network of cohesive, strong
ties, or a network of diverse, weak bridging ties I will get different benefits for myself
and my team from these different networks.
And I can change my network in order to facilitate what I'm trying to achieve.
So on the left hand [00:16:00] side you see the cohesive network with strong high bandwidth
ties, and I've noted there that this is a very good network for exploiting solutions
that we already know about.
It's efficient.
It works well.
People are very much in contact.
They're all highly tightly coordinated with another, and this is great for pounding through
on incremental innovation.
On the right what you see is much more the diverse, low bandwidth weak bridging tie
network is much more effective at exploring.
Finding new out of the box innovative solutions and breakthrough innovations.
And I'll just end by saying that my point on this slide is that form follows function.
If you are trying to set up a network for a particular purpose, it's important to consider
how the structure of that network is going to help you achieve different types of goals.
For instance, exploiting or exploring.
Incremental innovation, or breakthrough innovation.
Like Lisa said, there's no one correct network.
You have to match the form to the function to succeed.
Thank you guys very much.
I very much look forward to the discussion and the Q&A later on today.