Multi-Scale Subgraph Contrastive Learning
Authors: Yanbei Liu, Yu Zhao, Xiao Wang, Lei Geng, Zhitao Xiao
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments and parametric analysis on eight graph classification real-world datasets well demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | 1School of Life Sciences, Tiangong University 2School of Electronics and Information Engineering, Tiangong University 3School of Software, Beihang University |
| Pseudocode | Yes | Algorithm 1 The training process of the MSSGCL |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We adopt the TUDataset benchmark [Morris et al., 2020], which contains different types of graphs, i.e., molecules and social networks |
| Dataset Splits | Yes | We use 10-fold cross validation accuracy to report classification performance. Experiments are repeated 5 times. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using GIN as the encoder and Adam optimizer but does not specify version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In our framework, we set the global view size to be 80% of the whole and the local view size to be 20% of the whole for molecular graphs, and 90% of the global view size and 10% of the local view size for social networks. The measurement function between local views is composed of a 5-layer MLPs with batch normalization and RELU activation functions. Its output is fed into a Sigmoid function, which outputs a scalar to indicate the similarity between two local views. [...] we adopt GIN as the encoder, and a sum pooling is used as the readout function. [...] where λ1 and λ2 are hyper-parameters to balance different loss terms. |