Machine Learning on networks

Intervenant : Tina Eliassi-Rad

Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University. She is also on the faculty of the Network Science Institute. Prior to joining Northeastern, Professor Eliassi-Rad was an associate professor of computer science at Rutgers University; and before that a member of technical staff and principal investigator at Lawrence Livermore National Laboratory. She earned her PhD in Computer Sciences (with a minor in mathematical statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Professor Eliassi-Rad’s work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the US DOE Office of Science.

Détail du cours

Lecture 1: supervised and semi-supervied learning in networks. 

  • relational dependency
  • collective classification network sampling.

Lecture 2: unsupervised learning in networks.

  • community discovery
  • role discovery
  • graph representation learning
  • anomaly detection.

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