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The implementation of "Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network".

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Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network

PathNet

PathNet is a structure-aware path aggregation graph neural network that can deal with both homophily and heterophily graphs.

PathNet

This implementation of PathNet is based on Pytorch Geometric API.

Building the Project

File folders

splits: need to unzipped, contains the split data of "Cora, Cornell, Pubmed and Citeseer".

other_data: need to unzipped, contains the data of "Nba, Electronics, Bgp". (The splits are random generated in PathNet_run.py)

baseline: contains baseline models.

draw_figs: contains the code to draw the results' figs in the paper.

edge_input: contains the edge files used in preprocessing.

preprocess: contains the preprocessing code.

synthetic_data: contains the code for generate the labels of synthetic experiments.

Setup

This implementation is based on pytorch_geometric. To run the code, you need the following dependencies:

Path Sampler (preprocessing)

This part can be done before training. To generate the paths for dataset data_name (eg. Cora):

The first step is to use init_rw.py (which calls for compute_merw.py) to generate the ****.in as edge input which includes edge pairs and the transfer probability of each edge. (see examples like edge_input/cornell.in)

Secondly, in gen_merw.cpp, we change the parameter of [data_name], then compile and run gen_merw.cpp. The program will generate a file containing all paths.

Compile and run gen_merw.cpp for normal datasets.

g++ gen_merw.cpp -o gen_merw -g -Wall -O2 -mcmodel=medium
./gen_merw [data_name] [path_num] [path_length]

Revision guidance

The ss1 and ss2 in the main function are the input and output filenames. Feel free to revise the path of files to adapt to your running environment.

Generated paths example: ./gen_merw cornell 40 4

path_eg

The former 4 ids are paths consist of node_id and the later 4 number indicates the distance of each node on path to the target node.

Note: Fill in the data's name at the [data_name], the number of paths at the [path_num], the length of paths at the [path_length]. For example, when using the Cora dataset, ./gen Cora 40 4. Remember the data_name has to be the same as the file name in the edge_input without the postfix.in.

If a graph contains too many nodes, the file generated by the above method will be too large and occupy a large running memory. So we provide gen_epoch.cpp to generate corresponding paths for each epoch.

Compile and run gen_epoch.cpp for large datasets.

g++ gen_epoch_merw.cpp -o gen_epoch_merw -g -Wall -O2 -mcmodel=medium
./gen_epoch_merw [data_name] [path_num] [path_length]

Exploring New Data

For new testing on new data, you can process them into the format of the dataset in other_data folder and set the input parameter -nd as True when running the PathNet_run.py as follows.

Main model

Files

dataset.json: contains the path of split datasets.

save_json.ipynb: Adjust the paths in dataset.json. (Optional)

dataset.py: data usage code (imported in PathNet_run.py ).

PathNet_run.py: main code.

model usage

Note:

  1. For homophily dataset, the experiment setting follows the github of [Zhu]: https://github.com/GemsLab/H2GCN. The data is already splitted into splits in the splits folder. For heterophily dataset, we random split 10 times and the data is stored in other_data folder.

  2. Before using the code, please unzip the compressed files in all file folders. The file names in splits.zip are meaningless combinations of numbers and letters, relax. :)

  3. The mean accuracy and standard deviation is calculated by the 10 splits of dataset.

python PathNet_run.py -data=cornell -hid=128

Other hyperparameters can be delivered by command line arguments, such as -lr=0.005 and -data=citeseer. Other command line arguments can be found in PathNet_run.py. The trained model will be saved in 'saved_models' and the performance will be saved in 'results'.

For example: use python PathNet_run.py -data=cornell gets the result in result_for_cornell. The performance is also reported in the paper.

performance example

performance_eg

Total performance

performance

Model variants & parameters

Changing the PAGG class can deliver other variants. If you want to try other settings like path length or number of paths, you can first generate the according paths and change the hyperparameters.

Synthetic experiments

Synthetic graph generators are used to complete the synthesis experiment. At the beginning, all nodes with odd index and even index on the graph are attached with two different features. Synthetic label generators will search all paths with length k for each node (excluding the starting node), and there will be various path patterns. The index of the most path pattern will be selected as the label of the node.

We provide a synthetic label generator for Cora and Citeseer. The two generators will get a file with the same number of lines as the number of nodes in the original network, and each line represents the label of this node in the synthetic graph.

syn_cora: Compile and run gen_cora_syn.cpp

g++ gen_cora_syn.cpp -o gen_cora_syn -g -Wall -O2
./gen_cora_syn

syn_citeseer: Compile and run gen_citeseer_syn.cpp

g++ gen_citeseer_syn.cpp -o gen_citeseer_syn -g -Wall -O2
./gen_citeseer_syn

Contact

If you have any problem, feel free to contact with me. Thanks.

E-mail: yifeisun@zju.edu.cn

Citation

Please consider citing the following paper when using our code for your application.

@inproceedings{ijcai2022p310,
  title     = {Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network},
  author    = {Sun, Yifei and Deng, Haoran and Yang, Yang and Wang, Chunping and Xu, Jiarong and Huang, Renhong and Cao, Linfeng and Wang, Yang and Chen, Lei},
  booktitle = {Proceedings of the Thirty-First International Joint Conference on
               Artificial Intelligence, {IJCAI-22}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Lud De Raedt},
  pages     = {2233--2240},
  year      = {2022},
  month     = {7},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2022/310},
  url       = {https://doi.org/10.24963/ijcai.2022/310},
}

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