Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Authors: Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlow Net policies can efficiently find high-quality solutions. |
| Researcher Affiliation | Collaboration | Dinghuai Zhang Mila Hanjun Dai Google Deep Mind Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan Mila |
| Pseudocode | Yes | Algorithm 1 GFlow Net training algorithm |
| Open Source Code | Yes | Our implementation is open-sourced at https://github.com/zdh Narsil/GFlow Net-Comb Opt. |
| Open Datasets | Yes | For MIS problems, we follow the setup in the MIS benchmark from Böther et al. (2022)... we take the more complicated RB graphs (Xu & Li, 2000) following Karalias & Loukas (2020). For realistic data, we take the SATLIB dataset (Hoos et al., 2000)... For the other two tasks... we adopt BA graphs (Barabási & Albert, 1999) following Sun et al. (2022). |
| Dataset Splits | Yes | For all datasets, we use a validation set and a test set, both with 500 data points. ... We choose the hyperparameters of each algorithm based on the validation performance. |
| Hardware Specification | Yes | Regarding hardware, we use NVIDIA Tesla V100 Volta GPUs. |
| Software Dependencies | No | The paper mentions "pygurobi python package" and "cvxpy python package" but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | For the GFlow Net architecture, we use graph isomorphism network (Xu et al., 2019, GIN) with 5 hidden layers and 256 dimensional hidden size... We use the Adam optimizer with default 1 10 3 learning rate without hyperparameter tuning. ... with batchsize equals 64... The training keeps 20 epochs... we use an inverse temperature of 500 for all the tasks. |