version 0.15

Author: xianggebenben

GCNSI_source_prediction.png

@@ -1,9 +1,9 @@
 Metadata-Version: 2.1
 Name: GraphSL
-Version: 0.14
+Version: 0.15
 Summary: Graph Source Localization Approaches and Benchmark Datasets
 Home-page: https://github.com/xianggebenben/GraphSL
-Download-URL: https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.13.tar.gz
+Download-URL: https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.15.tar.gz
 Author: Junxiang Wang
 Author-email: junxiang.wang@alumni.emory.edu
 License: MIT
@@ -186,7 +186,7 @@ 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(test_dataset, diffusion_model, ivgd_model, thres)
+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
@@ -240,3 +240,5 @@ Version 0.12 adds the datasets downloader.
 Version 0.13 adds the visualization of source predictions.
 
 Version 0.14 uses the num_thres (i.e. number of thresholds to try) instead of specifying the thres_list (i.e. threshold list) for LPSI, GCNSI, IVGD and SLVAE. Moreover, GCNSI, IVGD and SLVAE are improved to run on CUDA if applicable.
+
+Version 0.15 makes all methods run on CUDA if applicable, replaces the diffusion model of IVGD and the encoder of SLVAE, and revises the generation of diffusion. 

GraphSL.egg-info/PKG-INFO

@@ -2,7 +2,6 @@ LICENSE
 README.md
 setup.cfg
 setup.py
-GraphSL/Evaluation.py
 GraphSL/Prescribed.py
 GraphSL/__init__.py
 GraphSL/utils.py
@@ -17,14 +16,9 @@ GraphSL/GNN/GCNSI/main.py
 GraphSL/GNN/GCNSI/model.py
 GraphSL/GNN/IVGD/__init__.py
 GraphSL/GNN/IVGD/correction.py
-GraphSL/GNN/IVGD/earlystopping.py
-GraphSL/GNN/IVGD/i_deepis.py
+GraphSL/GNN/IVGD/diffusion_model.py
 GraphSL/GNN/IVGD/main.py
-GraphSL/GNN/IVGD/preprocessing.py
-GraphSL/GNN/IVGD/training.py
 GraphSL/GNN/IVGD/validity_net.py
-GraphSL/GNN/IVGD/model/MLP.py
-GraphSL/GNN/IVGD/model/__init__.py
 GraphSL/GNN/SLVAE/__init__.py
 GraphSL/GNN/SLVAE/main.py
 GraphSL/GNN/SLVAE/model.py

GraphSL.egg-info/SOURCES.txt

@@ -60,7 +60,7 @@ def __init__(self, hidden_dim=32, num_layers=3, d=2):
         """
         super(I_GCN, self).__init__()
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-        self.iter_num = 10  # Number of iterations for fixed-point iteration in backward pass
+        self.iter_num = 5  # Number of iterations for fixed-point iteration in backward pass
         self.d = d  # Number of diffusion steps to consider in feature construction
 
         # Define the input layer with specified dimensions

GraphSL/GNN/IVGD/diffusion_model.py

@@ -215,3 +215,5 @@ Version 0.12 adds the datasets downloader.
 Version 0.13 adds the visualization of source predictions.
 
 Version 0.14 uses the num_thres (i.e. number of thresholds to try) instead of specifying the thres_list (i.e. threshold list) for LPSI, GCNSI, IVGD and SLVAE. Moreover, GCNSI, IVGD and SLVAE are improved to run on CUDA if applicable.
+
+Version 0.15 makes all methods run on CUDA if applicable, replaces the diffusion model of IVGD and the encoder of SLVAE, and revises the generation of diffusion.