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Retour à Réseaux sociaux et économiques : modèles et analyse

Avis et commentaires pour d'étudiants pour Réseaux sociaux et économiques : modèles et analyse par Université de Stanford

596 évaluations
130 avis

À propos du cours

Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: You can find a short introductory videao here:

Meilleurs avis

1 nov. 2017

Really enjoyed this course. The professor is really good and covers quite a lot of ground during the lectures. Good way to get into complex networks! Probably gonna do some studying on my own now :)

10 oct. 2020

Very important course. My suggestion to the Prof. if he can increase the course length and include more details that would be much better or he can come up with advance course on the same series.

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101 - 125 sur 127 Avis pour Réseaux sociaux et économiques : modèles et analyse

par Hakobyan Z

20 oct. 2017


par swapnil s

12 oct. 2016


par Andy P

18 oct. 2016


par anuj

30 mai 2017


par Stylianos T

24 févr. 2017

A very good introduction in social and economic networks.

I recommend this course to everyone that wants to learn how networks are formed, understand the basic concepts and get an intuition on the possible networks that he/she could form.

The professor is talking clearly so you won't have a problem in understanding him.

One thing that was missing for me was in Week 2 when he was talking about "eigenvector centrality", for me the most objective measure, the explanation was really poor and you could never understand the concept based on what the lesson offered.

par KM

21 août 2018

The chemistry disciplinary knowledge cautions the utilization of the idea of diffusion because diffusion in chemistry is more of systematic random process then the idea of diffusion in this lecture. If you could enhance and clarify the Week 4 lecture of the Praeto Efficiency, Utility, and Pairwise in additional examples the brevity of the lecture could build the idea into a few slides to sharpen the idea earlier. Think about adding more examples of the Centrality examples, I thought the Centrality was interesting.

par Carlson O

22 avr. 2017

Very comprehensive as an introductory course. The content is very actual and the lectures' flow is objective. Also, I liked the quiz inside the lectures as they helped in retain the subject. I have some hard difficulties with the mathematics as I'm very rusty with the mathematics (more than 30 years of rust). I'm from the compute science area so I would like to see more practice in algorithms. However, I would like to congratulate the Stanford University and Cousera teams for the course. Great job.

par Fernando I P M

3 août 2020

Buen curso en general. Sin embargo, podría estar más actualizado en términos de aplicaciones para el año 2020. Especialmente en trabajo con datos. Además, algunas evaluaciones adolecen de elementos que no están contenidos en el material, y si bien uno puede intuir a aplicar la teoría bajo otros contextos, muchas veces los resultados no son tan intuitivos, quedando algunas dudas respecto a esos contenidos más que clarificar dicho tópico.

par Alejandro A R

15 juil. 2018

Greatly insightful and resourceful content for future research. As a recent university graduate interested in graduate school I found the course challenging meaning determination and consistency contributed to the successful completion of the course. Rewatching lectures and seeking external support helped me comprehend concepts through application.

par GIAN M

4 mai 2020

Very interesting course, I raccomand it. It gives me a lot of notions and different view of networks, even if I'm already working with them. Very notable also the lot of references by which you can expand your knowledge and look for all the details of the field you are interested in.

Keep attention on the level, it is not for beginners :)

par Felipe O G C B

25 août 2016

It's a quiet complex topic in general terms. It is well covered, but In my opinion there should be at least an exercise per video, explaining something similar to the in-video questions. It should have a demonstrative part rather than just talking about it and showing the formula.

par Mateus d C C

19 janv. 2021

Great course, a bit complicated sometimes. The course is very structured and the classes are ordered is a natural way. The tests weren't hard and I think the course could focus more on experimental exercises.

par Justin K

10 déc. 2018

Excellent course. The labs are the best. Pajek and Gephi will be handy for network graphing and analyzing data. Thank you Professor Matthew Jackson. Your work is very good for reference.

par Simon N

6 juin 2020

Interesting survey of modern network theory, from Erdos-Renyi random graphs, to SIS ("flu") models, and games on networks. Rather academic at times, without the rigour.

par Harkeerat S

22 déc. 2016

The course is vast. The Professor is to the point and doesn't lack knowledge in his field.

I'd recommend this course for anyone interested in Economics. Loved it.

par Michael S

24 janv. 2019

I loved everything so far, the quiz questions are well selected, but, I believe there are some notions which should be explained further mathematically.

par Tianduo Z

1 nov. 2016

Very complex topic, very well presented. The materials are great! Would have been better to made mathematics pre-requisite clearer.

par Robertas K

31 mai 2020

Some quizzes have wrong answers, but overall it was quite a good introduction into network analysis.

par ND B

17 sept. 2020

I request Prof Jackson to speak more directly into the mike. At many points he is not audible.

par Sebastian H

15 oct. 2019

Hohes Anforderungsniveau, mathematische Fähigkeiten sind zwingend erforderlich.

par Jose

23 janv. 2018

This course is very good to introduce to the theory of networks

par XeRh

8 août 2020

It's very useful if you want to learn more anout network.

par Dheeraj B

4 oct. 2017

The discussion forums ought to be more responsive

par Navin N

10 déc. 2016

A bit tough, but really worth the effort.

par Muhammad I

10 oct. 2017

I'm sorry, but this course is really boring. Hopefully this lecture give more interactive approach (like animated presentation, pop up question, and so on) rather than voice of text in the slide