Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Filtration-Enhanced Graph Transformation
Authors: Zijian Chen, Rong-Hua Li, Hongchao Qin, Huanzhong Duan, Yanxiong Lu, Qiangqiang Dai, Guoren Wang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We theoretically and experimentally demonstrate that our solutions exhibit significantly better performance than the state-of-the art solutions for graph classification tasks. |
| Researcher Affiliation | Collaboration | Zijian Chen1 , Rong-Hua Li1 , Hongchao Qin1 , Huanzhong Duan2 , Yanxiong Lu2 , Qiangqiang Dai1 and Guoren Wang1 1Beijing Institute of Technology 2Tencent blockchan |
| Pseudocode | No | No structured pseudocode or algorithm blocks are present or explicitly labeled in the paper. |
| Open Source Code | Yes | Our source code are available at https://github.com/ Block Chan ZJ/Filtration-Enhanced-Graph-Transformation. |
| Open Datasets | Yes | We use 7 benchmark attributed graph datasets including 3 datasets with native edge weights (BZR MD, COX2 MD, ER MD) and 4 datasets with continuous vertex attributes (BZR, DHFR, ENZYMES, PROTEINS). All these 7 benchmark datasets are widely used in graph classification studies [Kriege and Mutzel, 2012; O Bray et al., 2021]. ... All the datasets are available at ls11-www.cs.tu-dortmund.de/ staff/morris/graphkerneldataset. |
| Dataset Splits | Yes | For graph kernels, we use CSVM as a classifier and perform 10-fold cross-validation. The evaluation process was repeated 10 times for each dataset and each method. For a fair comparison, we follow the standard data splits in [Errica et al., 2020]. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions that 'All graph kernels are implemented in Python using the Gra Ke L library', but does not specify version numbers for Python, GraKeL, or any other software dependencies. |
| Experiment Setup | Yes | The parameter C of the SVM is tuned from {10-3, , 103}. The layers of FEG/FES are chosen from {2, , 5} for full FEG/FES, and {2, , 10, 20, 50} for partial FEG/FES. ... All three GNNs are trained for 500 epochs with 50 epoch patience to early stop and hidden units of 64.The convolution layer numbers are selected from {2, 3, 4}. For Graph SAGE and GIN, we set the learning rate parameter as 0.001, batch size as 128, and dropout is chosen from {0, 0.5}. For Graph SNN, we use dropout of 0.5, batch size of 64 and learning rate chosen from {0.01, 0.001}. |