Machine Learning and Modelling for Social Networks

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GESS-COSS will offer a block course which covers computational and statistical methods to characterize the structure and dynamics of complex social networks. We cover methods such as clustering, classification, spectral analysis and Montecarlo and also specific applications to social network data and spreading processes on these networks. We discuss current research and ethical questions raised by applications.


The block course runs from 9am-12pm from the 8 May to the 12 May in the rooms list in the course catalogue (NB: these rooms are subject to change so check before the course starts).


For further information, please consult the course catalogue.


Date and Room Time Presenter Topic
08.05. Monday   Introduction to Machine Learning and Networks
HG E 3 9:00-9:30 Olivia Woolley Meza Introduction to the course and motivation (PDF, 1 MB)
HG E 3 9:30-11:00 Izabela Moise Introduction to Machine Learning: Supervised  (classification) and unsupervised  (clustering) (PDF, 120.1 MB)
HG E 3 11:00-12:00 Olivia Woolley Meza
Introduction to networks (PDF, 7.1 MB)
09.05. Tuesday   Spreading on Networks
ML H 37.1 9:00-10:30 Olivia Woolley Meza
Social networks, contagion, and influence (PDF, 4.1 MB)
ML H 37.1 10:30-11:30 Michael Mathioudakis, Aalto University Measuring polarization on social media Michael Mathioudakis; Aalto University (PDF, 2.5 MB)
ML H 37.1 11:30-12:00   Student discussion and forming groups for final project
10.05. Wednesday   Dynamics on Networks and text analytics
ML H 37.1 9:00-10:00 Nino Antulov-Fantulin Sampling, estimations and inference of dynamical processes on networks (PDF, 10.9 MB)
ML H 37.1 10:00-11:00 Izabela Moise
Big Data and Machine Learning - Twitter case-study (PDF, 28.7 MB)
ML H 37.1 11:00-12:00 Lloyd Sanders
Introduction to Sentiment Analysis (PDF, 2.5 MB)
11.05. Thursday   Opinion dynamics, Structural Inference and Link Prediction
LEE E 101 9:00-10:00 Michael Maes, University of Groningen  Does social influence on the web foster opinion polarization? Results from two field and one natural experiment
LEE E 101 10:00-11:00 Lloyd Sanders Introduction to link prediction (PDF, 5.2 MB)
LEE E 101 11:00-12:00 Nino Antulov-Fantulin
Detecting Network Structure with Machine Learning Methods (PDF, 5.6 MB)
12.05. Friday
  Tutorial and Evaluation Day
LEE E 101 9:00-10:00 Lloyd Sanders
Practical Data Analysis with Jupyter Notebooks (IPYNB, 60 KB)
LEE E 101 10:00-12:00   Evaluation

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Sun Jul 23 08:14:54 CEST 2017
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