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LICENSE

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+MIT License
+
+Copyright (c) 2025 Joshua Niemelä and Mustafa Al-Abdelamir
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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-# BachelorProject
-This project will explore the oversquashing problem in Graph Neural Networks (GNNs) and how it affects the ability for the model to learn long-range dependencies between nodes.
-The project will also explore the use of topological information in the form of the dataset's graph structure to improve the model's ability to learn long-range dependencies.  
-
-Supervised by: Raghavendra Selvan  
-
-## Random thoughts
-Attention is analogous to a fully-adjacent graph layer, therefore it is a special case of a GNN.
-Therefore, it should be possible to find some trade-off between losing computing efficiency and performance.
-
-Jacobians and stuff show that node to node insensitivity is equivalent to oversquashing and we coudl use this to measure insensitivity in a TDL graph
-
-Learnable topological representaiton.
-measuring distances between relevant nodes.
-
-replacing transformer attentoin with tdl based network
+# Bottlenecks within Graph Neural networks: A Study of Challenges and Mitigations
 
+This project will explore the oversquashing problem in Graph Neural Networks (GNNs) and how it affects the ability for the model to learn long-range dependencies between nodes.
+The project will also explore the use of topological information in the form of the dataset's graph structure to improve the model's ability to learn long-range dependencies.
 
+Supervised by: Raghavendra Selvan