
## A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai, Lixin Cui, Edwin Hancock
Keywords: 
ICML/2022/Proceedings/16245 - A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs.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?_

[9]

The authors state an algorithm they deploy a MATLAB implementation from with citation. A direct link is available in the citations. An overview of the framework is available in figure 2. No other details 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?_

[3]

(9/9)

The authors state the datasets used in section 4. A summary on the datasets is available in table 1. Citations are given for three, a direct link for the other six. No descriptions.

### 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 parameter settings for their kernel in section 4.1. They compute the H value on a grid and average it into the results. No other parameters applicable.

### 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 accuracy with standard error. Experiments are repeated 10 times with 10 fold-cross-validation (data split) and averaged.

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

[4]

Requries expertise in geometric dep learning and graph kernels.
