ccfbupt / asap Goto Github PK

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.

• 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

• 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

Example for PROTEINS dataset:

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

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.