| Proj No. | A3185-251 |
| Title | Robust representation learning in graph neural networks |
| Summary | A graph neural network (GNN) is a class of machine learning algorithms designed to handle data with an underlying graph structure. Graph representation learning has many applications, including in sensor networks, social networks, and transportation networks. However, GNNs are susceptible to various attacks include node and edge perturbations. This project investigates different GNN models for robust representation learning. It requires software implementation and familiarity with Python frameworks for deep learning. |
| Supervisor | Prof Tay Wee Peng (Loc:S1 > S1 B1A > S1 B1A 01, Ext: +65 67906280) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Information Systems (Loc: S2-B3a-06) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |