Graph Mixture Density Networks
Authors: Federico Errica, Davide Bacciu, Alessio Micheli
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on a new benchmark application that leverages random graphs for stochastic epidemic simulations. We show a significant improvement in the likelihood of epidemic outcomes when taking into account both multimodality and structure. The empirical analysis is complemented by two real-world regression tasks showing the effectiveness of our approach in modeling the output prediction uncertainty. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Pisa. Correspondence to: Federico Errica <federico.errica@phd.unipi.it>, Davide Bacciu <bacciu@di.unipi.it>, Alessio Micheli <micheli@di.unipi.it>. |
| Pseudocode | No | The paper describes the EM framework for training and provides mathematical equations, but it does not include a distinct pseudocode or algorithm block. |
| Open Source Code | Yes | We publicly release large datasets of stochastic SIR simulations... https://github.com/diningphil/graph-mixture-density-networks |
| Open Datasets | Yes | We publicly release large datasets of stochastic SIR simulations... We simulated the well-known stochastic SIR epidemiological model on Barabasi-Albert graphs of size 100 (BA-100)... We also carry out simulations for Erdos-Renyi graphs (ER-100)... We will evaluate our model on the large chemical benchmarks alchemy_full (Chen et al., 2019) and ZINC_full (Irwin et al., 2012; Bresson & Laurent, 2019) |
| Dataset Splits | Yes | We assess the performance of different models using a holdout strategy for all datasets (80%/10%/10% split). ...in these final training runs we use early stopping on a validation set extracted from the training set (10% of the training data). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Adam Optimizer' and 'the GIN convolution... adapted from Xu et al. (2019)', but does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | MDN: C {2,3,5}, hidden units per convolution {64}, neighborhood aggregation {sum}, graph readout {sum, mean}, α {1C, 1.05C}, epochs {2500}, Φi {Linear model}, Adam Optimizer with learning rate {0.0001}, full batch, patience {30}. GMDN: C {3,5}, graph convolutional layers {2,5,7}, hidden units per convolution {64}, neighborhood aggregation {sum}, graph readout {sum, mean}, α {1C, 1.05C}, epochs {2500}, Φi {Linear model}, Adam Optimizer with learning rate {0.0001}, full batch, patience {30}. |