Welcome to GraphSL’s documentation!
Contents:
Quickstart Guide
Installation
Install GraphSL using pip:
pip install GraphSL
Or, clone the repo (https://github.com/xianggebenben/GraphSL), and install requirements:
pip install -r requirements.txt
Usage
Now, you can import and use GraphSL in your Python code.
from GraphSL.GNN.SLVAE.main import SLVAE
from GraphSL.GNN.IVGD.main import IVGD
from GraphSL.GNN.GCNSI.main import GCNSI
from GraphSL.Prescribed import LPSI, NetSleuth, OJC
from GraphSL.utils import load_dataset, diffusion_generation, split_dataset,download_dataset,visualize_source_prediction
import os
curr_dir = os.getcwd()
# download datasets
download_dataset(curr_dir)
# load datasets ('karate', 'dolphins', 'jazz', 'netscience', 'cora_ml', 'power_grid')
data_name = 'karate'
graph = load_dataset(data_name, data_dir=curr_dir)
# generate diffusion
dataset = diffusion_generation(graph=graph, infect_prob=0.3, diff_type='IC', sim_num=100, seed_ratio=0.2)
# split into training and test sets
adj, train_dataset, test_dataset = split_dataset(dataset)
# LPSI
print("LPSI:")
lpsi = LPSI()
# train LPSI
alpha, thres, auc, f1, pred = lpsi.train(adj, train_dataset)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# test LPSI
metric = lpsi.test(adj, test_dataset, alpha, thres)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
# NetSleuth
print("NetSleuth:")
netSleuth = NetSleuth()
# train NetSleuth
k, auc, f1 = netSleuth.train(adj, train_dataset)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# test NetSleuth
metric = netSleuth.test(adj, test_dataset, k)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
# OJC
print("OJC:")
ojc = OJC()
# train OJC
Y, auc, f1 = ojc.train(adj, train_dataset)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# test OJC
metric = ojc.test(adj, test_dataset, Y)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
# GCNSI
print("GCNSI:")
gcnsi = GCNSI()
# train GCNSI
gcnsi_model, thres, auc, f1, pred = gcnsi.train(adj, train_dataset)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# visualize training predictions
pred = (pred >= thres)
visualize_source_prediction(adj,pred[:,0],train_dataset[0][:,0].numpy(),save_dir=curr_dir,save_name="GCNSI_source_prediction")
# test GCNSI
metric = gcnsi.test(adj, test_dataset, gcnsi_model, thres)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
# IVGD
print("IVGD:")
ivgd = IVGD()
# train IVGD diffusion
diffusion_model = ivgd.train_diffusion(adj, train_dataset)
# train IVGD
ivgd_model, thres, auc, f1, pred = ivgd.train(
adj, train_dataset, diffusion_model)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# visualize training predictions
pred = (pred >= thres)
visualize_source_prediction(adj,pred[:,0],train_dataset[0][:,0].numpy(),save_dir=curr_dir,save_name="IVGD_source_prediction")
# test IVGD
metric = ivgd.test(adj, test_dataset, diffusion_model, ivgd_model, thres)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
# SLVAE
print("SLVAE:")
slave = SLVAE()
# train SLVAE
slvae_model, seed_vae_train, thres, auc, f1, pred = slave.train(
adj, train_dataset)
print(f"train auc: {auc:.3f}, train f1: {f1:.3f}")
# visualize training predictions
pred = (pred >= thres)
visualize_source_prediction(adj,pred[:,0],train_dataset[0][:,0].numpy(),save_dir=curr_dir,save_name="SLVAE_source_prediction")
# test SLVAE
metric = slave.infer(test_dataset, slvae_model, seed_vae_train, thres)
print(f"test acc: {metric.acc:.3f}, test pr: {metric.pr:.3f}, test re: {metric.re:.3f}, test f1: {metric.f1:.3f}, test auc: {metric.auc:.3f}")
That’s it! You’re ready to start using GraphSL. You can check results on the Jupyter notebook tutorial.ipynb from the repo.
If you use this package in your research, please consider citing our work as follows:
@article{wang2024joss,
year = {2024},
author = {Wang Junxiang, Zhao Liang},
title = {GraphSL: A Open-Source Library for Graph Source Localization Approaches and Benchmark Datasets},
journal = {preprint, arXiv:2405.03724}
}
Contact
We welcome your contributions! If you’d like to contribute your datasets or algorithms, please submit a pull request consisting of an atomic commit and a brief message describing your contribution.
For a new dataset, please upload it to the data folder of the repo. The file should be a dictionary object saved by pickle. It contains a key “adj_mat” with the value of a graph adjacency matrix (sprase numpy array with the CSR format).
For a new algorithm, please determine whether it belongs to presribed methods or GNN-based methods: if it belongs to the prescribed methods, add your algorithm as a new class in the GraphSL/Prescribed.py. Otherwises, please upload it as a folder under the GraphSL/GNN folder. Typically, the algorithm should include a “train” function and a “test” function, and the “test” function should return a Metric object.
Feel free to Email me(junxiang.wang@alumni.emory.edu) if you have any questions. Bug reports and feedback can be directed to the Github issues page.