
## Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains
Vishwaraj Doshi, Jie Hu, Do-Young Eun
Keywords: 
Award: Outstanding Paper
ICML/2023/Proceedings/1186 - Self-Repellent Random Walks on General Graphs - Achieving Minimal Sampling Variance via Nonlinear Markov Chains.pdf
Project URL: nan

### 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]

No implementation 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]

(1/1)

The authors use wiki-Vote graph as data, provide a citation on it and a direct link in the references. They describe the graph and state the size but not much else.

### 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 the process is paramterised by alpha and state the values in the legend of figure 1.

### 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 measure TV and MSEU (eq. 20/21, described). Results are averaged over 500 runs. Test set not applicable.

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

[5]

Requries expertise on markov chains.
