Abstract: As the use of machine learning (ML) algorithms in network science increases, so do the problems related to explainability, transparency, fairness, privacy, and robustness, to name a few. In this talk, I will give a brief overview of the field and present recent work from my lab on the (in)stability and explainability of node embeddings, attacks on ML algorithms for graphs, and equality in complex networks.
Noyce Conference Room
US Mountain Time
Our campus is closed to the public for this event.
Tina Eliassi-RadExternal Professor