| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | DSAA 6000B |
| DESCRIPTION | Graph, as a very expressive model, has been widely used to model real-world entities and their relationships in application-specific networks. In this course, students will gain a thorough introduction to the basics of graph theories, as well as cutting-edge research in deep learning for graphs. The topics include graph embeddings, graph neural networks, graph clustering models, graph generative models, adversarial attacks on graphs, graph reasoning, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6299) | Tu 03:00PM - 05:50PM | Rm 134, E1 | ZHANG, Yongqi | 60 | 0 | 60 | 0 |