Schedule
This is tentative course schedule, changes will be announced in the class if any and will be updated here as well.
Date | Title | Slides | Video | Extra Reading |
---|---|---|---|---|
Week 1 | ||||
04-08-22 | Introduction to Graph Machine Leanring + Course logistics | |||
05-08-22 | Basics of Graphs | Tutorial-1 | ||
Week 2 | ||||
11-08-22 | Basics of Graphs (Traditional Graph ML) | Tutorial-2 | Chapter-2: from GRL by W.L. Hamilton | |
12-08-22 | Spectral Methods for Graph Clustering | Some Proofs | ||
Week 3 | ||||
18-08-22 | Institite Foundation Day (Class Suspended) | |||
19-08-22 | Holiday | |||
Week 4 | ||||
25-08-22 | Deep Learning Review : Guest Lecture (Dr. Jiaul H. Paik) | Chapter-3: Foundation of Deep Learning from the book DLG | ||
26-08-22 | Graph Embedding: Concurrence Preserving Methods (Introduction) | |||
Week 5 | ||||
01-09-22 | Graph Embedding: Structure Preserving Methods (DeepWalk and Node2vec) | Tutorial-3 | ||
02-09-22 | Graph Embedding: Structure Preserving Methods (Struc2vec) | |||
Week 6 | ||||
08-09-22 | Heterogeneous Network Embedding & metapath2vec | |||
09-09-22 | Graph Neural Network, Deep Graph representation: Generalized Framework | |||
Week 7 | ||||
15-09-22 | Neural Networks in detail: Graph Convolution Neural Network | |||
16-09-22 | GNN: GraphSAGE | |||
Week 8 & Week 9 | ||||
22-09-22
to 30-09-22 |
Mid Semester Examination | |||
Week 10 | ||||
06-10-22 | Holiday | |||
07-10-22 | Knowledge Graph Embedding | Will be Rescheduled | ||
Week 11 | ||||
13-10-22 | Graph Attention Networks (GAT), Edge Conditioned Convolution (ECC) | Tutorial-4 | ||
14-10-22 | Graph Pooling: Flat and Hierarchical, Downsampling, DiffPool, EigenPool | |||
Week 12 | ||||
20-10-22 | Heterogeneous Graph Representation Learning, Multi-Dimensional Graph Neural Networks | |||
21-10-22 | Signed Graph Neural Networks | |||
Week 13 | ||||
27-10-22 | Knowledge Graph Embedding, Translational Distance Models, Semantic Matching Models | |||
28-10-22 | Reasoning over Knowledge Graphs | |||
Week 14 | ||||
03-11-22 | Data Mining Applications |