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  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




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