Date of Submission
2025
Date of Award
6-2025
Institute Name (Publisher)
Indian Statistical Institute
Document Type
Master's Dissertation
Degree Name
Master of Technology
Subject Name
Computer Science
Department
Electronics and Communication Sciences Unit (ECSU-Kolkata)
Supervisor
Das, Swagatam
Abstract (Summary of the Work)
Graph Neural Networks (GNNs) are highly effective in many real-world tasks, such as molecular property prediction, modeling protein structures, analyzing user-item relationships, and making link predictions. What sets them apart is their ability to learn meaningful representations by capturing not just the features of individual nodes, but also the overall structure of the graph they belong to. This expressive strength allows GNNs to model complex relationships more accurately. In this work, we take a step further by introducing geometric transformations aimed at improving how GNNs handle spatial information. In particular, we focus on angular aggregation methods that maintain rotational consistency, helping the model deliver more stable and reliable predictions even when the input orientation changes
Control Number
CS2330
DOI
https://dspace.isical.ac.in/items/fc5723ce-5ef7-4784-a01a-ed3e3743da87
DSpace Identifier
http://hdl.handle.net/10263/7570
Recommended Citation
Dey, Suranjan, "Enhancing Expressive Power Of Graph Neural Networks Using Geometric Transformations" (2025). Master’s Dissertations. 444.
https://digitalcommons.isical.ac.in/masters-dissertations/444