
## On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lió, Michael Bronstein
Keywords: 
ICML/2023/Proceedings/24077 - On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology.pdf
Project URL: nan

[X] Theoretical

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[1]

Although there is an experiment being done in this work, we still consider this paper to be theoretical as the experimental section is only a very small part of it and is not a new method the authors introduce rather an analysis of behaviour to suppor their theoretical findings.

### Data
_Given the data description in the documentation, how much effort take to either: Find the same dataset the authors used, or similar datasets and defend the comparability, or acquire one from scratch?_

[1]

(1/1)

N/A

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for their results, and compare against their budgetary constraints?_

[1]

N/A

### Experimental Procedure
_Given the experimental set-up of the work, how difficult is it to set up a new experiment, similar to those presented in the original work, with the same procedure?_

[1]

N/A

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying on the available documentation?_

[7]

Requires experience with geometrical deep learning.
