Doctoral Speaking Skills Talk - Meng-Chieh (Jeremy) Lee December 4, 2024 2:00pm — 3:30pm Location: In Person - Reddy Conference Room, Gates Hillman 4405 Speaker: MENG-CHIEH (JEREMY) LEE, Ph.D. Student, Computer Science Department, Carnegie Mellon University https://mengchillee.github.io/ Accurate, Robust, and Interpretable Graph Mining How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining tasks, but their generalizability to various graph scenarios is limited due to the difficulty of training, hyperparameter tuning, and the selection of a model itself. In this talk, I will present a carefully-designed simple model SlimG for solving semi-supervised node classification. It exhibits the following desirable properties: accurate, robust, fast, scalable, and interpretable. Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.
