
## Graph Contrastive Learning with Reinforcement Augmentation
Ziyang Liu, Chaokun Wang, Cheng Wu
Keywords: Data Mining: DM: Mining graphs, Machine Learning: ML: Representation learning, Machine Learning: ML: Reinforcement learning, Machine Learning: ML: Self-supervised Learning
IJCAI/2024/Proceedings/0246 - Graph Contrastive Learning with Reinforcement Augmentation.pdf

### 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?_

[10]

The authors do not provide a source for their implementation. They provide pseudo code on their method and some minor illustration / framework diagrams of the method/process. No implementation details are given.

### 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?_

[2]

(14/14)

The authors use public data sets and provide the sources with citations. Each dataset has a small field description, and statistics provided in table 2. No direct link is given. A larger description on their tasks would be welcome for comparability but can be extracted from the citations.

### 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?_

[3]

For each data set, the hyperparameter vluaes are stated in section 5.1. It is not specified how these values are acquired.

### 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]

The authors state they use 10-fold cross validation with data divison percentages as their procedure. The authors state they use accuracy as metric, and how the results are aggregated. 

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

[7]

Requires an understanding of geometric deep learning to understand the method, as well as the task. The absence of the implementation means a lot is left for the independent investigators to figure out, and thus much more source material must be used in order to reproduce the method.
