The Infinite Contextual Graph Markov Model
Authors: Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On 8 graph classification tasks, we show that ICGMM: i) successfully recovers or improves CGMM s performances while reducing the hyperparameters search space; ii) performs comparably to most end-to-end supervised methods.The Infinite Contextual Graph Markov Model; We compare ICGMM against CGMM as well as end-to-end supervised methods on eight different graph classification tasks, following a fair, robust and reproducible experimental procedure (Errica et al., 2020). |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, University of Pisa, Italy 2NEC Laboratories Europe, Heidelberg, Germany 3Work primarily done as a Ph D student at the University of Pisa. |
| Pseudocode | Yes | For the interested reader, we report the ICGMM complete Gibbs sampling equations and pseudo-code in Appendix A and B, respectively. (Algorithm 1 is presented in Appendix B) |
| Open Source Code | Yes | 1The code to rigorously reproduce our results is provided here: https://github.com/diningphil/i CGMM. |
| Open Datasets | Yes | All datasets are publicly available (Kersting et al., 2016) and their statistics are summarized in Appendix C. |
| Dataset Splits | Yes | It consists of an external 10-fold cross validation for model assessment, followed by an internal hold-out model selection for each of the external folds. Stratified data splits were already provided |
| Hardware Specification | No | A fair time comparison between all models requires to look at the time to result using the same resources in our case CPUs. No specific CPU or GPU models, memory amounts, or detailed computer specifications are provided. |
| Software Dependencies | No | Finally, we relied on Pytorch Geometric (Fey & Lenssen, 2019) for the implementation of our method. No specific version number is provided for Pytorch Geometric. |
| Experiment Setup | Yes | Number of layers {5, 10, 15, 20}, Unibigram aggregation {sum, mean}, Gibbs sampling iterations {100} for ICGMM and {10, 20, 50, 100} for ICGMMαγ, α0 {1, 5}, γ {1, 2, 3} (Only for ICGMMαγ); Adam optimizer with batch size 32 and learning rate 1e-3, Hidden units {32, 128}, L2 regularization {0., 5e-4}, epochs {2000}, early stopping on validation accuracy, with patience 300 on chemical tasks and 100 on social ones. |