The wild mice contact network collected by Barbara Konig and Anna Lindholm and analysed in our paper Flow stability for dynamic community detection is available on Zenodo here:
The code for our algorithm MADAN published in the paper: Gutiérrez-Gómez, L. Bovet, A. & Delvenne, J.-C. Multi-scale Anomaly Detection on Attributed Networks. Proceedings of the AAAI Conference on Artificial Intelligence. Vol 34 No 01: AAAI-20 Technical Tracks 1 (2020) is available here.
Fake news influence
The dataset containing the retweet networks and the tweet IDs that have a URL directing toward a news outlet website of the corresponding media category is available on OSF here: doi.org/10.17605/OSF.IO/J6PKS
It is used in our paper: Bovet, A. & Makse, H. A. Influence of fake news in Twitter during the 2016 US presidential election. Nat. Commun. 10, 7 (2019).
The code used for the analysis of this dataset is also available on OSF doi.org/10.17605/OSF.IO/E4TVH.
The curated list of website spreading fake and extremely biased news used in our paper is available here.
This dataset contains the tweet IDs of 170 million tweets from 11 million users posting about the election between June 1st 2016 until November 9th 2016.
It is used in our paper: Bovet, A., Morone, F. & Makse, H. A. Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump. Sci. Rep. 8, 8673 (2018).
Twitter social network and sentiment analysis
You will find here the ipython notebooks for the lecture I gave at The Graduate Center of the City University of New York in 2017.
The notebooks also cover the basics about how to use tweepy to connect to the Twitter API and collect tweets.