CSL7870: Learning on Graphs and its Applications (Fall 2024)

Offered in Fall 2024 Semester

Credits

3-0-0 [3]
700 Level

Slot N

Tue, Thu 8:00AM - 8:50AM
Fri 5:00PM - 5:50PM

LMS

https://moodle.iitj.ac.in/
Enrollment Code: vy5f9w

Syllabus

Introduction: Complex Systems, Graph Data Structure, Graph Representations, Spectral graph theory, Real world networks and applications.

Graph Representation Learning: Node Embeddings, Graph Convolutional Networks, Design Space, Inductive Representation Learning, Graph Attention Networks, Hierarchical Graph Representation Learning, Graph Embeddings, Over smoothing Issues, Expressiveness of GNN, GNN vs CNN, Graph Representation Learning vs Graph Signal Processing.

GNN Applications: Heterogeneous and Multilayer graphs, Knowledge graphs, Reasoning over knowledge graphs, Graphs and LLMs, Neural subgraph matching, GCN for Recommender Systems, Node Classification, Graph Classification, Community Detection, Link Prediction and Causality.

Large Scale Graphs: Dealing with large graphs, Cluster-GCN, Simplifying Graph Convolutional Networks.

Learning Materials

Textbook

Reference Books

  • Network Science, by Albert-Laszlo Barabasi, (Cambridge University Press - August 2016) freely available under the Creative Commons licence. http://www.networksciencebook.com/

Self Learning Material

Grading Policy
Interactive Activity Assignments Group Activity
10% 20% 10%
Minor Major
20% 40%

Interactive Activity

There will be activities in the LMS or in Classroom where students are provided with different tasks to complete. In many cases we will perform these activities during the class on mobile or laptop. Each of these activity may have different grading rules which will be intemated to you.

Assigments

There will be 2 to 3 assignments related to course content. The assignments are expected to be completed alone. Students are encouraged to discuss with the fellow mates. The submission in such cases should mention the name and roll no of the people who work together. Best two will be counted for the grading even if we only have 2 assignments during the semester.

Group Activity

This will be a practical project where students will work together for a common goal. There will be demo and Q&A at the end of the semester for evaluation.

Minor and Major

Minor and Major exam will be with pen and paper, offline, closed book. The questions will be mixed of objective and subjective.

Attendance Policy
As per the institute policy. You are expected to attend 75% of classes. Maximum two weeks of continuous leave. In other words I expect you to see in the classroom for about 28 to 30 classes out of 39. Those taking long leave, for more than 5 classes, please inform me or course TA.
Plagiarism tolerance

Plagiarism tolerance is 7% from single source and 15% cumulative, anything more will reduce your marks as follows:

  • Any logical/conceptual/formulation plagiarism: zero marks
  • Other form of plagiarism (above 50%): zero marks
  • Otherwise: Percentage of plagiarism will be deducted from the obtained mark

These policy will apply for any assignments, hands on and practical work. Copying from friends and internet both treated similarly.