Welcome to the 'case studies in networks' GitHub page. Here you will find links to resources for this course --- links to papers I mention, helpful books and software packages. In addition, you will find the slides in the main folder on the morning of the course.
The coding content of this will be done through R. Before starting the course; it would be good to download the R and Rstudio. I have provided a video on setting up R and Rstudio here to make things a little easier [here].
This GitHub repository can be downloaded as a zip file to have all the content in one place by clicking on the green 'code' button and then selecting 'download zip'. I will be updating the code and slides before the course, so maybe leave this step until the morning of the course.
Talk soon.
- The software setup video can be found here.
- The slide deck is in the main folder.
- Integrating sentiment and social structure to determine preference alignments: the Irish Marriage Referendum [here].
- The Spread of Obesity in a Large Social Network over 32 Years [here].
- Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic [here].
- Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks [here].
- This is a fantastic paper!
- Dynamical Systems on Networks: A Tutorial [here].
- "I Wanted to Predict Elections with Twitter and all I got was this Lousy Paper" --- A Balanced Survey on Election Prediction using Twitter Data [here].
- The author Daniel Gayo-Avello has a load of other interesting papers on the subject.
- Heart and soul: Sentiment strength detection in the social web with sentistrength [here]
- An Introduction to ROC Analysis [here].
- The Structural Virality of Online Diffusion [here].
- Mathematical modeling of complex contagion on clustered networks by David J. P. O'Sullivan*, Gary J. O'Keeffe, Peter G. Fennell and James P. Gleeson [here].
- The spread of behavior in an online social network experiment by Damon Centola [here].
- Free online version of Network Science by Albert-László Barabási [here].
- Free online version of R for Data Science by Garrett Grolemund and Hadley Wickham [here].
- Free online version of Text Mining with R by Julia Silge and David Robinson [here].
- Free online version of R Cookbook by James (JD) Long and Paul Teetor [here].
- Free version of The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman [here].
- Free version of Computer Age Statistical Inference by Trevor Hastie and Bradley Efron [here].