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Announcements
Final project peer evaluation and discussion
Hi there!
Sorry about all the extra email. I managed to auto-send an email from last year.
The peer evaluation period for the projects ended today, and you can see some of the awesome projects here https://class.coursera.org/sna-004/forum/thread?thread_id=414.
Lada
Sorry about all the extra email. I managed to auto-send an email from last year.
The peer evaluation period for the projects ended today, and you can see some of the awesome projects here https://class.coursera.org/sna-004/forum/thread?thread_id=414.
Lada
Wed 3 Dec 2014 6:01 AM CET
Week 9: final
Week 9! Just the final exam remains. It is available now and you have 1 week to complete it, until Monday Dec. 8th at 9:30pm PT. It is timed; once you start you have 2 hours to complete it. So please don't click 'Start exam now' until you are ready, because you only get to submit it once. The exam is constructed to test your understanding of the main concepts we covered in the class. It does not involve the use of SNA software. Finally, it is important that you do not discuss the exam with others before the deadline.
Good luck!
Good luck!
Mon 1 Dec 2014 6:28 AM CET
Week 8: network resilience
Hello,
This week we're covering network resilience to random failure (of nodes or edges) and intentional attack.
If NetLogo is giving you trouble in HW8, please see this helpful post by Andrew Eliasz: https://class.coursera.org/sna-004/forum/thread?thread_id=393.
This concludes the new material for the course, and next week will have the final exam.
For those of you who have completed the final project: apologies for the error in the peer assessment deadline, it has been fixed and moved to Dec. 2, so you have a bit of time to evaluate your peers' work. Also, if you're willing to share your project, please post your write-up here: https://class.coursera.org/sna-004/forum/thread?thread_id=414 for all of us to enjoy.
For those of you looking to continue learning about Social Network Analysis, here are some resources:
Online courses:
Networks, Crowds, and Markets: https://www.edx.org/course/networks-crowds-cornellx-info2040x#.VHc5G2TF_dc
Social and Economic Networks: https://www.coursera.org/course/networksonline
Networked Life: https://www.coursera.org/course/networks
and relatedly: Mining Massive Data Sets: https://www.coursera.org/course/mmds
You can also continue your studies at a university. Many now have interdisciplinary programs (masters and PhD) incorporating social network analysis. Here are a few:
University of Michigan School of Information: https://www.si.umich.edu/content/phd
Network Science at Northeastern: http://www.northeastern.edu/networkscience/
Network Science at CEU: http://courses.ceu.hu/programs/phd/doctor-philosophy-network-science
Take care,
This week we're covering network resilience to random failure (of nodes or edges) and intentional attack.
If NetLogo is giving you trouble in HW8, please see this helpful post by Andrew Eliasz: https://class.coursera.org/sna-004/forum/thread?thread_id=393.
This concludes the new material for the course, and next week will have the final exam.
For those of you who have completed the final project: apologies for the error in the peer assessment deadline, it has been fixed and moved to Dec. 2, so you have a bit of time to evaluate your peers' work. Also, if you're willing to share your project, please post your write-up here: https://class.coursera.org/sna-004/forum/thread?thread_id=414 for all of us to enjoy.
For those of you looking to continue learning about Social Network Analysis, here are some resources:
Online courses:
Networks, Crowds, and Markets: https://www.edx.org/course/networks-crowds-cornellx-info2040x#.VHc5G2TF_dc
Social and Economic Networks: https://www.coursera.org/course/networksonline
Networked Life: https://www.coursera.org/course/networks
and relatedly: Mining Massive Data Sets: https://www.coursera.org/course/mmds
You can also continue your studies at a university. Many now have interdisciplinary programs (masters and PhD) incorporating social network analysis. Here are a few:
University of Michigan School of Information: https://www.si.umich.edu/content/phd
Network Science at Northeastern: http://www.northeastern.edu/networkscience/
Network Science at CEU: http://courses.ceu.hu/programs/phd/doctor-philosophy-network-science
Take care,
Thu 27 Nov 2014 6:16 AM CET
Week 6 processes on networks
Hi there,
This week we are going to be taking advantage of all the things we know about network structure to understand how it affects the processes that occur over them.
I also forgot to send out an email last week about Week 5 and small world networks (how we can have interesting local structure in networks while still having any two nodes a mere few hops away from one another). So the deadline for HW 5 is extended to coincide with HW 6.
If you're working on a project, I've opened a thread I'll be checking for questions you might have and will try to answer them through Monday, Nov. 17th: https://class.coursera.org/sna-004/forum/thread?thread_id=357.
Take care!
This week we are going to be taking advantage of all the things we know about network structure to understand how it affects the processes that occur over them.
I also forgot to send out an email last week about Week 5 and small world networks (how we can have interesting local structure in networks while still having any two nodes a mere few hops away from one another). So the deadline for HW 5 is extended to coincide with HW 6.
If you're working on a project, I've opened a thread I'll be checking for questions you might have and will try to answer them through Monday, Nov. 17th: https://class.coursera.org/sna-004/forum/thread?thread_id=357.
Take care!
Mon 10 Nov 2014 4:12 PM CET
Week 4: community structure
Hello!
Weeks 4 and 5 cover the sub-structure that can be found in networks. Community structure and clustering often arise from commonalities among the nodes. For example, in social networks, two individuals who share a friend in common might form a triad when they are introduced by that friend. Or a group of individuals might have many ties between them because of a shared interest, activity, or other context. Discovering communities can thus help us learn about the underlying process shaping the network. On the other hand, networks with much interesting "local" structure also typically have a few random or long range edges, which help to connect any two individuals in just a small number of hops.
HW4 and (optionally) the 3rd programming assignment are due this week.
Weeks 4 and 5 cover the sub-structure that can be found in networks. Community structure and clustering often arise from commonalities among the nodes. For example, in social networks, two individuals who share a friend in common might form a triad when they are introduced by that friend. Or a group of individuals might have many ties between them because of a shared interest, activity, or other context. Discovering communities can thus help us learn about the underlying process shaping the network. On the other hand, networks with much interesting "local" structure also typically have a few random or long range edges, which help to connect any two individuals in just a small number of hops.
HW4 and (optionally) the 3rd programming assignment are due this week.
Wed 29 Oct 2014 8:33 AM CET
Week 3: centrality
This week is all about network centrality. Your todos: lectures, HW3, and optionally the 2nd programming assignment.
The hangout this week will be about choosing a topic for the optional programming project (10am PDT on Thursday), and will be coordinated through the G+ community https://plus.google.com/communities/113582289701193478586.
Enjoy!
The hangout this week will be about choosing a topic for the optional programming project (10am PDT on Thursday), and will be coordinated through the G+ community https://plus.google.com/communities/113582289701193478586.
Enjoy!
Tue 21 Oct 2014 4:18 PM CEST
Week 2: network models
Hi there!
Here's what's happening in the week ahead:
We are learning about network models. This may seem like an abrupt change of pace from the empirical mode we were in in week 1, but it's important. Models help us to understand how networks form and help us appreciate their structure and how this structure can influence things such as diffusion happening on/through them. HW 2 in particular will explore diffusion processes, so consider doing the homework even if you're not taking the course for credit.
To accommodate folks with different backgrounds, week 2 lectures parts A and C have "regular" and "advanced" versions. Pick one version to watch, depending on your background in probability distributions and differential equations. Both versions cover the same ground and both teach you what you need to know in order to do HW 2.
The first and second homework assignments are due next Monday (a week from now). If you haven't started working on them yet, there is still some time!
If you are planning to do the programming option track, the first programming assignment is due this Monday as well, and you may want to start thinking about project topics.
I will have a 1/2 hour "office hour" this week via hangout in our Google+ community on Wednesday Oct. 15th at 10am PDT: https://plus.google.com/u/0/communities/113582289701193478586. I hope those participating can each share their favorite network visualizations. Details on how to join the hangout will be posted in the community and a link to the 'On Air' recording will be posted under announcements once the office 1/2 hour starts.
Have fun exploring network models!
Here's what's happening in the week ahead:
We are learning about network models. This may seem like an abrupt change of pace from the empirical mode we were in in week 1, but it's important. Models help us to understand how networks form and help us appreciate their structure and how this structure can influence things such as diffusion happening on/through them. HW 2 in particular will explore diffusion processes, so consider doing the homework even if you're not taking the course for credit.
To accommodate folks with different backgrounds, week 2 lectures parts A and C have "regular" and "advanced" versions. Pick one version to watch, depending on your background in probability distributions and differential equations. Both versions cover the same ground and both teach you what you need to know in order to do HW 2.
The first and second homework assignments are due next Monday (a week from now). If you haven't started working on them yet, there is still some time!
If you are planning to do the programming option track, the first programming assignment is due this Monday as well, and you may want to start thinking about project topics.
I will have a 1/2 hour "office hour" this week via hangout in our Google+ community on Wednesday Oct. 15th at 10am PDT: https://plus.google.com/u/0/communities/113582289701193478586. I hope those participating can each share their favorite network visualizations. Details on how to join the hangout will be posted in the community and a link to the 'On Air' recording will be posted under announcements once the office 1/2 hour starts.
Have fun exploring network models!
Mon 13 Oct 2014 8:36 AM CEST
Welcome to Social Network Analysis!
Hello!
We're just about ready to get started. For a quick overview of the course, please check the syllabus.
Social network analysis is a highly interdisciplinary subject, but we hope that there will be something of interest there for everyone, and that everyone will learn the core concepts and methods.
In preparation please download two free network analysis tools:
NetLogo
Gephi
In addition, for the NetLogo application, you will need to install the NW extension, as documented in the course wiki.
You will notice that during the lectures, some quizzes will provide a link to a page with a NetLogo model embedded in it as a Java applet. Unfortunately, browsers can no longer run Java applets, so you'll need to run the model from NetLogo directly (details here).
In week 1 we will talk about why SNA is useful, tools for SNA, and breeze through a demo of Gephi. The week 1 HW is under Assignments tab in the left navigation bar, the rest of the homework will be under 'Homework', just to keep you on your toes. Just the first homework assignment relies on some custom grading scripts and so tends to be fickle. Please be patient while it is being graded.
There are 2 tracks for the course. In the first track, no programming experience is required. You complete 8 homework assignments, and take the final exam. In the second track, you have in addition, 3 programming assignments and a project (which will be peer-assessed). The second track allows you to earn a certificate ``with distinction".
If you have questions, please check out the FAQ. If they are not answered there, please post them to the forum. The course wiki has additional information on everything from supplementary reading to lecture notes to software tools, and you should feel free to contribute to it. Ohad Bregman, your community TA, and I will be checking in on the forum and wiki and helping out.
We will occasionally be tweeting about the course as @snacourse, and have a Google+ community as well through which we will coordinate hangouts.
We are thrilled that so many of you have decided to give this course a try. Social (and other) networks everywhere, prepare to be analyzed!
-- Lada
P.S. We are experimenting with making all lectures and assignments (except for the final exam) available immediately, with staggered due dates.
We're just about ready to get started. For a quick overview of the course, please check the syllabus.
Social network analysis is a highly interdisciplinary subject, but we hope that there will be something of interest there for everyone, and that everyone will learn the core concepts and methods.
In preparation please download two free network analysis tools:
NetLogo
Gephi
In addition, for the NetLogo application, you will need to install the NW extension, as documented in the course wiki.
You will notice that during the lectures, some quizzes will provide a link to a page with a NetLogo model embedded in it as a Java applet. Unfortunately, browsers can no longer run Java applets, so you'll need to run the model from NetLogo directly (details here).
In week 1 we will talk about why SNA is useful, tools for SNA, and breeze through a demo of Gephi. The week 1 HW is under Assignments tab in the left navigation bar, the rest of the homework will be under 'Homework', just to keep you on your toes. Just the first homework assignment relies on some custom grading scripts and so tends to be fickle. Please be patient while it is being graded.
There are 2 tracks for the course. In the first track, no programming experience is required. You complete 8 homework assignments, and take the final exam. In the second track, you have in addition, 3 programming assignments and a project (which will be peer-assessed). The second track allows you to earn a certificate ``with distinction".
If you have questions, please check out the FAQ. If they are not answered there, please post them to the forum. The course wiki has additional information on everything from supplementary reading to lecture notes to software tools, and you should feel free to contribute to it. Ohad Bregman, your community TA, and I will be checking in on the forum and wiki and helping out.
We will occasionally be tweeting about the course as @snacourse, and have a Google+ community as well through which we will coordinate hangouts.
We are thrilled that so many of you have decided to give this course a try. Social (and other) networks everywhere, prepare to be analyzed!
-- Lada
P.S. We are experimenting with making all lectures and assignments (except for the final exam) available immediately, with staggered due dates.
Mon 6 Oct 2014 2:20 PM CEST
Demographic Survey
Hello all, thank you for signing up for the course. I hope you are as excited to begin as we are!
In the meantime, I'm hoping that you could take a few minutes to complete the Coursera Demographic Survey. The data from this survey will help us better understand our student population, and improve each offering of the course.
Best wishes,
Lada
In the meantime, I'm hoping that you could take a few minutes to complete the Coursera Demographic Survey. The data from this survey will help us better understand our student population, and improve each offering of the course.
Best wishes,
Lada
Sun 5 Oct 2014 6:00 PM CEST