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Del curso dictado por Princeton University

Networks Illustrated: Principles without Calculus

43 calificaciones

Princeton University

Networks Illustrated: Principles without Calculus

43 calificaciones

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. All the features of this course are available for free. It does not offer a certificate upon completion.

De la lección

PageRank by Google

In this lesson, we will take a look at PageRank, Google's famous algorithm for ordering the results on its search page. PageRank is a prime example of how coming up with the right "ranking" of a set of items is a difficult yet important question in networking.

New Word in the Dictionary5:01

Search Engines7:03

The "Random Surfer"7:38

Importance Equations5:31

PageRank Example Calculation10:40

PageRank Example Summary5:14

Dangling Nodes & Disconnected Graph6:22

Robust Ranking3:24

Conoce a los instructores

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

All right, so this is one of the shorter lectures that we will have in this

course. And, but we still did cover quite a bit

of material in general. And so we looked at first, the idea of

search engines. We had a specific focus on Google Search,

but we looked at how Google came to be. We looked at the difference between

relevance and importance, right, so relevance is how close your query matches

to the given page, or how close a given page matches to your query.

Like how many keywords on that page are the same as in your query.

And the important score is independent upon what's actually searched.

It's, it's just dependent upon the webgraph.

And hyperlink connectivity among the webpages.

Then we looked at the idea of a webgraph, and graphs in general.

And so, we looked at how we'd shoot the webpages as nodes and hyperlinks as the

links in that graph and how that was, that's really important when you're

dealing with important scores specifically.

Then we looked at PageRank, we had a really high emphasis on PageRank here.

And we saw how they calculate their importance score terms, in terms of those

recursive equations, recursive relationships, and simultaneous equations

that we use to, to spread importance score, right?

So, we calculate the equations, we wrote one out for each node, right?

So you only had the number of equations being equal to the number of unknowns.

again, we saw a recursive equations, and then we looked at two fixes that we need

to make to that dangling nodes and disconnected graphs.

Dangling nodes are nodes that simply don't point to anyone else, and they

cause us to have no solution to the PageRank computation, and disconnected

graphs, are just graphs with more than one connected component, meaning there's

more than one subgraph. Or two different portions of the graph

that don't connect to each other at all. And so some major themes we saw here.

First, we looked at graphs and graphs are huge.

They're a huge thing mathematical notion of graphs.

And we're going to use them again. especially, as we move towards the

Internet and discussing the Internet, we'll talk about graphics routers and

that'll be another fun topic. And we looked again at randomization.

And we've seen randomization before. We saw that with, in the WI-FI chapter,

we, we looked at choosing a random contension window size and here that was

in terms of just randomly selecting and entering URL, adding some randomization

to that. So, that's a recurring theme throughout

many network topics and different networking algorithms, the idea of

randomization. And then the idea, again, of consensus,

right? So, here we had to find the unique

consensus, among the webpages. To determine what the rank order should

be in order to make the output as useful as possible.

But consensus, as we said, it's hard because first of all, it's not that easy

to solve a huge, huge system of equations, right?

And we have to guarantee that that system of equations is going to have a unique

solution. So, coming up with that consensus is not

the easiest task in the world, and even beyond this in adwords we said Google

selling ad space. We won't have time to cover this in this

course, but they have to come up with consensus among bidders, and bidders for

those ad spaces and determine how to charge accordingly.

And again, there's no right answer to that question.

It's just what they think works the best. And then and Wikipedia has to follow the

process of the rough consensus when they, whenever there's edits on a page and

there's conflicting opinions on the edits and which ones are correct.

they may establish some sort of a voting grounds in order to do that.

And we look at we look at those in much more detail in the optional reading

associated with this course. Unfortunately, we won't have time to

cover them, even though they are very interesting topics.

but yeah, so, we'll leave you with that and I hope you enjoyed the lecture.

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