
## Bayesian Spiked Laplacian Graphs
Leo L Duan, George Michailidis, Mingzhou Ding
Keywords: 
JMLR/2023/Proceedings/201206 - Bayesian Spiked Laplacian Graphs.pdf
Project URL: https://github.com/leoduan/BayesSpikedLaplacian

### 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?_

[2]

The authors provide a URL to the implementation on the JMLR website (https://github.com/leoduan/BayesSpikedLaplacian) and at the end of section 1 as well. The readme is empty. No installation instructions. Okay comments.

### 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]

(2/2)

The authors evaluate the method on synthetic data in section 5 and real world data in section 6. The authors state they generate graphs and give parameters/sizes of the data in 5.1, and code is available in the implementation. The real world data is described in section 6 and cited but no direct link.

### 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]

The authors state a method in algorithm 1. Its parameter free.

### 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 present their results with mean + std dev over 50 experiments. Metric is accuracy. 

### 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 matrix mathematics / linear algebra and laplacian graphs.
