Graph-Powered machine learning is becoming an important trend in Artificial Intelligence, transcending a
lot of other techniques. Using graphs as basic representation of data for ML purposes has several
advantages: (i) the data is already modeled for further analysis, explicitly representing connections
and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a
single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation
performances and quality. The talk will discuss these advantages and present applications in the context
of recommendation engines and natural language processing.