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來自 Princeton University 的課程

Networks Illustrated: Principles without Calculus

39 個評分

Princeton University

Networks Illustrated: Principles without Calculus

39 個評分

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.

從本節課中

Influencing People in Social Networks

In this lesson, we will continue with our theme of influence, now paying more attention to people's social networks. We will discuss different ways of measuring importance and a popular model for influence spread in social networks like Facebook and Twitter.

Facebook & Twitter9:55

Who is "important?"6:35

Social graph2:34

Degree centrality4:18

Closeness centrality: Part I5:00

Closeness centrality: Part II10:28

Betweenness centrality: Part I4:59

Betweenness centrality: Part II13:57

Contagion: Part I6:17

Contagion: Part II7:01

Cluster density2:43

Marketing strategies10:50

與講師見面

Christopher Brinton
Lecturer
Electrical Engineering
Mung Chiang
Professor
Electrical Engineering

So let's briefly summarize what we've covered in this lecture.

We talked about social networking sites, and that's

what we talked about in the beginning, how

we motivated it, specifically Facebook and Twitter, which

are the two dominant social networking sites today.

And we looked at differences between the two, especially in terms

of the graph and the way the graph is set up.

Because on Facebook, the links are

bidirectional, whereas in Twitter they are unidirectional,

because just because I follow you doesn't mean that you have to follow me.

We looked at charting influential power on social

networking sites, and how certain companies are trying

to reconstruct the graphs of these sites in

order to figure out who the influential people are.

And then we also talked about marketing campaigns

and how you could stage some campaigns to

try to seed some people with a given

product in order to be able to influence others.

And the ones that you want to choose are going to be

the ones that are the most influential or the most important.

We've talked in a significant portion of this lecture about

measuring influence, which is the analysis portion of the problem.

How do we quantify that?

And specifically we talked about many centrality measures.

We talked about three of them in particular.

And there's many more than three.

The three that we talked about were degree centrality

which is simply counting the degree of the node.

We've talked about closeness centrality which is finding the average shortest path

distance from the node to all the other nodes in the graph and

then taking the reciprocal of that, so it's looking at how close

the given node is to all of its, other, nodes in the network.

And we also looked at between-ness centrality which is looking at

the, number of shortest paths that each node is lying on.

So that's looking more at the connectivity of the graph.

So there's no clear answer as to which of the centrality measures is the best.

To a certain extent, when doing any of these problems

you should look at all of the centrality measures, but

clearly measures such as closeness and between the centrality are

going to be more informative than those such as degree centrality.

We also then turn our attention to the synthesis problem.

So once we can figure out who the important people are, how do we

use that to be able to influence

adoption and adoption fractions within a population.

And we looked at one particular model of

contagion, and in order to do contagion, we said

that every node needed to have a flipping

threshold, and the nodes in this case are people.

Different people will not necessarily have the same threshold, but we made

that assumption that thresholds is certainly

going to be influenced by the product.

So, given the product if it's cheaper or

attractive it's going to have a lower threshold, meaning

that you're quicker to adopt it the more of your peers that you see adopting it.

Then we turn that to say, how would we use the

influential people in the network in order to seed a contagion and

seed people with the given products in order to maximize the

fraction of people at the end who had actually adopted the product.

So major themes that we've covered here, which are present

throughout other lectures as well, the first is importance scores.

So centrality measures really are a measure of

importance of a node in a different way.

And indeed we could have looked at another measure of importance here, we

could have used the, the page rank

computations scores as being another centrality measure.

So, figuring out which nodes in a graph are

important is definitely present in many of the lectures.

We looked at topology models as opposed to population-based models.

We looked at population-based models under the context of YouTube, previously.

And this was, topology-based, actually leveraging the

social graphic connectivity among users to do influence.

And finally again, that crowds are not

so wise, so under certain circumstances we can

have the wisdom of crowds work out if

we have independence of assumptions, if you remember

from back when we discussed Amazon and Netflix,

but in these conditions where the network is

now in place Last time we talked about

information cascade and the fallacy of the crowds.

And this time it's really about, the social graph and, having influence

from your peers and having that type of a network effect go on.

Thanks, and see you in the next lecture.

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