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https://github.com/PurdueMINDS/JanossyPooling


https://github.com/PurdueMINDS/JanossyPooling

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# Janossy Pooling

### Authors: Ryan L. Murphy and Balasubramaniam Srinivasan



## Overview:

This is the code for [Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs](https://arxiv.org/abs/1811.01900).



We evaluate different Janossy Pooling models for tasks similar to those found in [Deep Sets](https://github.com/manzilzaheer/DeepSets) and [GraphSAGE](https://github.com/williamleif/GraphSAGE).

Our implementation follows these, as well as the reference [PyTorch implementation of GraphSAGE](https://github.com/williamleif/graphsage-simple/). The latter repo also contains two datasets that we use.



The first set of tasks is to perform arithmetic on sequences of integers: sum, range, unique sum, unique count, and variance.  Note that these functions are all permutation-invariant (symmetric).



The second set of tasks learns vertex embeddings in graphs for vertex classification.  The data are described below.



Please see the supplementary section for a brief description and summary of the code. 



## Requirements

* PyTorch 0.4.0 or later - which can be downloaded [here](https://www.pytorch.org)

* Python 2.7



## How to Run

For the sequence based tasks, please use the following format:

* python train.py -m "model name" -t "task" -l "number of hidden layers in rho" -lr "learning rate" -b "batch size"

* Permitted models are {deepsets,janossy_nary,lstm,gru}

* Permitted tasks are {sum,range,unique_sum,unique_count,variance}



For the graph based tasks, we have provided an example below:

* python train_janossy_gs.py --model_type=nary --dataset=cora    --num_samples_one=5  --num_samples_two=5  --inf_permute_number=20 --seed=1 --embedding_dim_one=256 --embedding_dim_two=256 --num_test=1000 --num_val=500 --lr=0.005 --num_batches=100 --batch_size=256 --output_dir=<> --input_dir=<>



We recommend training these models on a GPU.



## Data

* For the arithmetic tasks, the data is generated on the fly as described in our paper. You can adjust the number of training, test and validation examples used.

* For the graph tasks, the following datasets were used:

  - [PPI](https://snap.stanford.edu/graphsage/ppi.zip)

  - Cora, [available at the GraphSAGE PyTorch repo](https://github.com/williamleif/graphsage-simple/)

  - Pubmed, [available at the GraphSAGE PyTorch repo](https://github.com/williamleif/graphsage-simple/)



Cora and Pubmed are described in Sen et al., 2008, and PPI in Zitnik and Leskovec, 2017.  They are described in Hamilton 2017.

Please see [our paper](https://arxiv.org/abs/1811.01900) for further details, including our test/train/validation splits. 



## Questions

Please feel free to reach out to Ryan L. Murphy (murph213  at  purdue.edu) if you have any questions.



## Citation

If you use this code, please consider citing:

```

@inproceedings{

murphy2018janossy,

title={Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs},

author={Ryan L. Murphy and Balasubramaniam Srinivasan and Vinayak Rao and Bruno Ribeiro},

booktitle={International Conference on Learning Representations},

year={2019},

url={https://openreview.net/forum?id=BJluy2RcFm},

}

```