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A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)
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# A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493) by Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel. This implementation gets 100% accuracy on node-selection bAbI task 4, 15, and 16. Their official implementation are available in the [yujiali/ggnn](https://github.com/yujiali/ggnn) repo on GitHub. <img src="images/ggnn.png"> ## What is GGNN? - Solve graph-structured data and problems - A gated propagation model to compute node representations - Unroll recurrence for a fixed number of steps and use backpropogation through time - An output model to make predictions on nodes ## Requirements - python==2.7 - PyTorch>=0.2 ## Run Train and test the GGNN: ``` python main.py --cuda (use GPUs or not) ``` Suggesting configurations for each task: ``` # task 4 python main.py --task_id 4 --state_dim 4 --niter 10 # task 15 python main.py --task_id 15 --state_dim 5 --niter 10 # task 16 python main.py --task_id 16 --state_dim 10 --niter 150 ``` ## Results I followed the paper, randomly picking only 50 training examples for training. Performances are evaluated on 50 random validation examples. | bAbI Task | Performance | | ------| ------ | | 4 | 100% | | 15 | 100% | | 16 | 100% | Here's an example of bAbI deduction task (task 15) <img src="images/babi15.png" width=700> ## Disclaimer The data processing codes are from official implementation [yujiali/ggnn](https://github.com/yujiali/ggnn). ## TODO - [ ] GraphLevel Output ## References - [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493), ICLR 2016 - [yujiali/ggnn](https://github.com/yujiali/ggnn)
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