GraphDIVE: Graph Classification by Mixture of Diverse Experts
Authors: Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on molecular benchmark datasets demonstrate the effectiveness of the proposed approach. and 5 Experiments |
| Researcher Affiliation | Academia | Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2 University of Chinese Academy of Sciences |
| Pseudocode | No | The paper describes its methods using mathematical formulations (e.g., Eq. 2, 4, 5, 9, 10) but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | To foster reproducible research, our code is made publicly available at https://github.com/CRIPAC-DIG/DIVE. |
| Open Datasets | Yes | We conduct experiments on four benchmark molecular property prediction datasets [Hu et al., 2020], including HIV, PCBA, BACE, and BBBP. |
| Dataset Splits | Yes | Specifically, we follow the original scaffold train-validation-test split with the ratio of 80/10/10. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or specific computer configurations used for running experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and implementing methods like GCN and GIN, but does not provide specific version numbers for programming languages, libraries, or software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | For hyper-parameter setting, we train the model using Adam optimizer [Kingma and Ba, 2015] with initial learning rate of 0.001. For HIV and PCBA datasets, we train the network for 200 epochs in light of the scale of the dataset. Moreover, for all the other datasets, we train the model for 100 epochs. According to the average performance on the validation dataset, we use grid-search to find the optimal value for K (i.e., the number of views), M (i.e., the number of experts), and λ. We set the hyper-parameter space of K and M as {2, 3, 4, 5, 6, 7, 8} and the hyper-parameter space of λ as {0.001, 0.01, 0.1, 1, 10}, respectively. Besides, the hyperparameter space of α is {0, 1} and the hyper-parameter space of p is {1, 2, 3, + }. The hyper-parameter space of τ and γ is {0.001, 0.01, 0.1, 1, 10, 100}. |