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From the course by Princeton University

Networks Illustrated: Principles without Calculus

39 ratings

Princeton University

Networks Illustrated: Principles without Calculus

39 ratings

What makes WiFi faster at home than at a coffee shop? How does Google order its search results from the trillions of webpages on the Internet? Why does Verizon charge $15 for every GB of data we use? Is it really true that we are connected in six social steps or less? These are just a few of the many intriguing questions we can ask about the social and technical networks that form integral parts of our daily lives. This course is about exploring the answers, using a language that anyone can understand. We will focus on fundamental principles like “sharing is hard”, “crowds are wise”, and “network of networks” that have guided the design and sustainability of today’s networks, and summarize the theories behind everything from the social connections we make on platforms like Facebook to the technology upon which these websites run. Unlike other networking courses, the mathematics included here are no more complicated than adding and multiplying numbers. While mathematical details are necessary to fully specify the algorithms and systems we investigate, they are not required to understand the main ideas. We use illustrations, analogies, and anecdotes about networks as pedagogical tools in lieu of detailed equations. Please note that per Princeton University policy, no certificates, credentials or reports are awarded in connection with this course.

From the lesson

Viral Videos on YouTube

What does it take for a video to become "viral" on YouTube? In this lesson, we will take a look at some of the key factors and models that have been used to explain this phenomenon. At the core is the notion of information cascade in a network, which is the counterpart to the wisdom of crowds.

YouTube timeline4:50

Viral style and video recommendation7:36

Defining "viral"3:18

Information cascade & sequential decision making8:24

Number-Guessing Thought-Experiment3:11

First, second, and third "guessers"7:36

Analyzing cascades: Part I13:21

Analyzing Cascades: Part II10:25

Emperor's New Clothes4:34

Considerations5:14

Meet the Instructors

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

So, now we can take a step back

and summarize what we've covered in this lecture.

We really motivated the entire thing with, YouTube videos

and looking at ways that we may be able

to viralize YouTube videos, or trying to figure out

why specific videos become viral, why others do not.

And in doing so, we saw that there

is definitely a gap between theory and practice.

But we were also able to define exactly what we do mean when we say viral.

Is that the video is eventually going to reach a very high total view count.

It's going to have a short time before it starts to ramp

up and a long time that it ramps up for at least.

We also looked at a co-visitation account, which

is how YouTube does recommendation for similar videos.

To, trying to figure out which videos to

recommend based upon which one is currently watched.

As opposed to other, more sophisticated

algorithms like Page Rank and Netflix style.

A movie recommendation which would not be very reliable in this case.

And we also looked at ways of quantifying popularity, or just different

classes of popularity and what makes items or in this case videos popular.

First case being that it could have some inherent or

intrinsic value to you, independent upon what other people think.

And the second being that there may be some network effects going on.

Whether that's a positive or a network effect.

You know, in the case of a fax machine.

We saw a positive network effects being something that

has more value, the more people that own it.

And also we saw information cascade being that you kind of

ignore your own instinct about something and just follow suit with it.

So that brought us to the main portion of the lecture where we drilled down into

what we actually mean by information cascade and

we took a look at sequential decision making specifically.

And in looking at sequential decision making we

saw the difference between public and private signals.

And private signal being what you believe internally and public action

being what you're actually going to release and show to the public.

We also saw how you may ignore your own private instinct in favor of public action

if you see enough of those public actions

being displayed, which can then trigger an information cascade.

It's either correct or an incorrect cascade.

We took a specific look at the number-guessing thought-experiment,

which is a model from the early 1990s in which

we saw that such cascades can be triggered by

only having two public actions being displayed as the same.

In which the objective, again, the objective behind that was

to guess whether the value was zero or one, and everybody

would go up to a blackboard, be shown a private

signal by a moderator, and then try to make a guess.

We also saw how easy it can be in certain circumstances to break a cascade.

If for instance, it, it could take only a group of

two people, that we saw, each having the same private signal.

And then one of them leaking what their private signal

is, in order to reverse, or to break the cascade.

And finally, let's look at the themes we saw in this lecture.

The first was a network effect, right?

So we, we took a look specifically at a positive

network effect in a case of Wikipedia or a fax machine.

Being that the value of something is going to increase or the

popularity is going to increase based upon the number of people that use it.

And we also saw a negative network effect as

well in terms of negative or incorrect information cascades.

We also looked at positive feedback, and we saw how as

opposed to negative feedback that here we're responding to some public action.

So, as we see more of an action we're more

likely to do it, which then increases the amount of

people doing the action, which then increases the likelihood the

next person will come and do it, and so forth.

As opposed to negative feedback where we're trying

to go the opposite direction of some error signal.

Finally, we saw that crowds are not so wise, in these cases, that's

right and when we have the wisdom of crowds, we need independence, and we

need unbiased guesses, but here, when all you, all the information's released to

the public, nothing's independent anymore, and the

wisdom of the crowds entirely breaks down.

Thanks, see you in the next lecture.

[BLANK_AUDIO]

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