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AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
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Readme.md

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Conference Paper

Source code for AAAI 2020 paper: ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representation

Overview of ASAP: ASAP initially considers all possible local clusters with a fixed receptive field for a given input graph. It then computes the cluster membership of the nodes using an attention mechanism. These clusters are then scored using a GNN. Further, a fraction of the top scoring clusters are selected as nodes in the pooled graph and new edge weights are computed between neighboring clusters. Please refer to Section 4 of the paper for details.

File Descriptions

  • main.py - contains the driver code for the whole project
  • asap_pool.py - source code for ASAP pooling operator proposed in the paper
  • asap_pool_model.py - a network which uses ASAP pooling as pooling operator
  • le_conv.py - source code for LEConv GNN used in the paper
  • requirements.txt - contains the required libraries used in this project

Dependencies

  • Compatible with PyTorch 1.0 and Python 3.x.
  • Dependencies can be installed using requirements.txt.

Can be installed using the following command:

pip install -r requirements.txt

Training a model from scratch

Example for PROTEINS dataset:

python main.py -data PROTEINS -batch 128 -hid_dim 64 -dropout_att 0.1 -lr 0.01

Hyperparameters to reproduce reported scores in the paper

Dataset Batch Size Hidden Dimension Dropout Learning rate
PROTEINS 128 64 0.1 0.01
FRANKENSTEIN 128 32 0 0.001
NCI1 128 128 0 0.01
NCI109 128 128 0 0.01
DD 64 16 0.3 0.01

Citation:

Please cite the following paper if you found it useful in your work.

@article{ranjan2019asap,
  title={ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations},
  author={Ranjan, Ekagra and Sanyal, Soumya and Talukdar, Partha Pratim},
  journal={arXiv preprint arXiv:1911.07979},
  year={2019}
}

For any clarification, comments, or suggestions please create an issue or contact Ekagra.

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