Filtration-Enhanced Graph Transformation
Authors: Zijian Chen, Rong-Hua Li, Hongchao Qin, Huanzhong Duan, Yanxiong Lu, Qiangqiang Dai, Guoren Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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}. |