| KEYWORD |
Outliers and Anomaly Detection in Online Social Networks
keywords BIG DATA ANALYSIS, DATA SCIENCE, GRAPHS, MARKOV CHAINS, MODELLING, SOCIAL NETWORKS, STATISTICS
Reference persons MARCO MELLIA, MARTINO TREVISAN, LUCA VASSIO
External reference persons Francesca Soro (DET)
Research Groups SmartData@PoliTO, Telecommunication Networks Group
Thesis type DATA ANALYSIS, MODELLING
Description Online social networks, such as Instagram and Facebook, allow users to interact and debate with each other. In this reserarch, the candidate will analyze large quantities of data collected from social networks, possibily using big data techniques (such as Pyspark). Then, the student will model the phenomena that characterize the usage of the social network. In particular, the student will propose a methodology to find outliers and anomaly detection. Finally, these models will be validated and will help recognizing the differences in usage and forecast possible future scenarios.
Required skills Modellazione, analisi dati, python programing, preferentially big data (pyspark)
Notes Contact luca.vassio@polito.it
Deadline 16/07/2024
PROPONI LA TUA CANDIDATURA