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Volver a Social and Economic Networks: Models and Analysis

Opiniones y comentarios de aprendices correspondientes a Social and Economic Networks: Models and Analysis por parte de Universidad de Stanford

641 calificaciones
146 reseña

Acerca del Curso

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:

Principales reseñas

2 de jul. de 2021

I was new to network theory but the concepts were very well articulated. A whole new way of looking at what makes social relationships, favor exchange(s) and social networks work. Well worth the time.

1 de nov. de 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 :)

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101 - 125 de 142 revisiones para Social and Economic Networks: Models and Analysis

por Sebastián F

22 de dic. de 2018

Very nice and useful course.


1 de jul. de 2020

I am enjoying the course

por Sourav M

24 de may. de 2020

Great course..!!

por John B

10 de sep. de 2017

Wonderful course

por Phan T B T

31 de may. de 2021

Great course!!1

por Rijul K

3 de dic. de 2018

greaaaat course

por Богдан

25 de nov. de 2016

Very intresting

por Anand A R

27 de abr. de 2020

Great Course!

por Mojtaba A

27 de oct. de 2017

Great teacher

por Antonio C

14 de oct. de 2020

big course

por Christiano F d C

4 de oct. de 2020

Very good!

por Mohammad N C

17 de mar. de 2021


por Pablo E

12 de feb. de 2018


por Zaruhi H

20 de oct. de 2017


por swapnil s

12 de oct. de 2016


por Andy P

18 de oct. de 2016


por anuj

30 de may. de 2017


por Stylianos T

24 de feb. de 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.

por KM

21 de ago. de 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.

por Carlson O

22 de abr. de 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.

por Fernando I P M

3 de ago. de 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.

por Alejandro A R

15 de jul. de 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.

por Gian M C

4 de may. de 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 :)

por Felipe O G C B

25 de ago. de 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.

por Mateus d C C

19 de ene. de 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.