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xianggebenben · Apr 4, 2024 · bd9f5a6 · bd9f5a6
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diff --git a/GraphSL.egg-info/PKG-INFO b/GraphSL.egg-info/PKG-INFO
@@ -1,22 +1,20 @@
 Metadata-Version: 2.1
 Name: GraphSL
-Version: 0.6
+Version: 0.7
 Summary: Graph Source Localization Approaches and Benchmark Datasets
 Home-page: https://xianggebenben.github.io/Junxiang_Wang.github.io/
-Download-URL: https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.6.tar.gz
 Author: Junxiang Wang
 Author-email: junxiang.wang@alumni.emory.edu
 License: MIT
+Download-URL: https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.6.tar.gz
 Keywords: Graph Diffusion,Graph Source Localization,Prescribed Methods,GNN Methods,Benchmark
+Platform: UNKNOWN
 Classifier: Development Status :: 3 - Alpha
 Classifier: License :: OSI Approved :: MIT License
 Classifier: Programming Language :: Python :: 3
 Classifier: Programming Language :: Python :: 3.10
+Requires-Python: >=3.9
 License-File: LICENSE
-Requires-Dist: numpy
-Requires-Dist: networkx
-Requires-Dist: ndlib
-Requires-Dist: torch
-Requires-Dist: scikit-learn
-Requires-Dist: scipy
-Requires-Dist: torch_geometric
+
+UNKNOWN
+
diff --git a/GraphSL.egg-info/top_level.txt b/GraphSL.egg-info/top_level.txt
@@ -1,4 +1 @@
-Evaluation
-GNN
-Prescribed
-data
+
diff --git a/Evaluation.py → GraphSL/Evaluation.py b/Evaluation.py → GraphSL/Evaluation.py
diff --git a/GNN/GCNSI/__init__.py → GraphSL/GNN/GCNSI/__init__.py b/GNN/GCNSI/__init__.py → GraphSL/GNN/GCNSI/__init__.py
diff --git a/GNN/GCNSI/main.py → GraphSL/GNN/GCNSI/main.py b/GNN/GCNSI/main.py → GraphSL/GNN/GCNSI/main.py
@@ -2,10 +2,10 @@
 from scipy.sparse import csgraph,coo_matrix
 import copy
 import numpy as np
-from GNN.GCNSI.model import GCNSI_model
-from Prescribed import LPSI
+from GraphSL.GNN.GCNSI.model import GCNSI_model
+from GraphSL.Prescribed import LPSI
 from sklearn.metrics import roc_auc_score,f1_score,accuracy_score,precision_score,recall_score
-from Evaluation import Metric
+from GraphSL.Evaluation import Metric
 class GCNSI:
     """
     Implement the Graph Convolutional Networks based Source Identification (GCNSI).

diff --git a/GNN/GCNSI/model.py → GraphSL/GNN/GCNSI/model.py b/GNN/GCNSI/model.py → GraphSL/GNN/GCNSI/model.py
diff --git a/GNN/IVGD/__init__.py → GraphSL/GNN/IVGD/__init__.py b/GNN/IVGD/__init__.py → GraphSL/GNN/IVGD/__init__.py
diff --git a/GNN/IVGD/correction.py → GraphSL/GNN/IVGD/correction.py b/GNN/IVGD/correction.py → GraphSL/GNN/IVGD/correction.py
diff --git a/GNN/IVGD/earlystopping.py → GraphSL/GNN/IVGD/earlystopping.py b/GNN/IVGD/earlystopping.py → GraphSL/GNN/IVGD/earlystopping.py
diff --git a/GNN/IVGD/i_deepis.py → GraphSL/GNN/IVGD/i_deepis.py b/GNN/IVGD/i_deepis.py → GraphSL/GNN/IVGD/i_deepis.py
diff --git a/GNN/IVGD/main.py → GraphSL/GNN/IVGD/main.py b/GNN/IVGD/main.py → GraphSL/GNN/IVGD/main.py
@@ -2,13 +2,13 @@
 import numpy as np
 import torch
 import torch.nn as nn
-from GNN.IVGD.correction import correction
-from GNN.IVGD.i_deepis import i_DeepIS, DiffusionPropagate
-from GNN.IVGD.training import FeatureCons, get_idx_new_seeds
-from GNN.IVGD.model.MLP import MLPTransform
-from GNN.IVGD.training import train_model
+from GraphSL.GNN.IVGD.correction import correction
+from GraphSL.GNN.IVGD.i_deepis import i_DeepIS, DiffusionPropagate
+from GraphSL.GNN.IVGD.training import FeatureCons, get_idx_new_seeds
+from GraphSL.GNN.IVGD.model.MLP import MLPTransform
+from GraphSL.GNN.IVGD.training import train_model
 from sklearn.metrics import roc_auc_score,f1_score,accuracy_score,precision_score,recall_score
-from Evaluation import Metric
+from GraphSL.Evaluation import Metric
 import torch.optim as optim
 import warnings
 warnings.filterwarnings("ignore")

diff --git a/GNN/IVGD/model/MLP.py → GraphSL/GNN/IVGD/model/MLP.py b/GNN/IVGD/model/MLP.py → GraphSL/GNN/IVGD/model/MLP.py
diff --git a/GNN/IVGD/model/__init__.py → GraphSL/GNN/IVGD/model/__init__.py b/GNN/IVGD/model/__init__.py → GraphSL/GNN/IVGD/model/__init__.py
diff --git a/GNN/IVGD/preprocessing.py → GraphSL/GNN/IVGD/preprocessing.py b/GNN/IVGD/preprocessing.py → GraphSL/GNN/IVGD/preprocessing.py
diff --git a/GNN/IVGD/training.py → GraphSL/GNN/IVGD/training.py b/GNN/IVGD/training.py → GraphSL/GNN/IVGD/training.py
diff --git a/GNN/IVGD/validity_net.py → GraphSL/GNN/IVGD/validity_net.py b/GNN/IVGD/validity_net.py → GraphSL/GNN/IVGD/validity_net.py
diff --git a/GNN/SLVAE/__init__.py → GraphSL/GNN/SLVAE/__init__.py b/GNN/SLVAE/__init__.py → GraphSL/GNN/SLVAE/__init__.py
diff --git a/GNN/SLVAE/main.py → GraphSL/GNN/SLVAE/main.py b/GNN/SLVAE/main.py → GraphSL/GNN/SLVAE/main.py
@@ -2,10 +2,10 @@
 import torch
 import torch.nn.functional as F
 import numpy as np
-from GNN.SLVAE.model import VAE,GNN,DiffusionPropagate
+from GraphSL.GNN.SLVAE.model import VAE,GNN,DiffusionPropagate
 from torch.optim import Adam
 from sklearn.metrics import roc_auc_score,f1_score,accuracy_score,precision_score,recall_score
-from Evaluation import Metric
+from GraphSL.Evaluation import Metric
 class SLVAE_model(nn.Module):
     """
     Source Localization Variational Autoencoder (SLVAE) model combining VAE, GNN, and propagation modules.

diff --git a/GNN/SLVAE/model.py → GraphSL/GNN/SLVAE/model.py b/GNN/SLVAE/model.py → GraphSL/GNN/SLVAE/model.py
diff --git a/GNN/__init__.py → GraphSL/GNN/__init__.py b/GNN/__init__.py → GraphSL/GNN/__init__.py
diff --git a/Prescribed.py → GraphSL/Prescribed.py b/Prescribed.py → GraphSL/Prescribed.py
@@ -4,7 +4,7 @@
 import torch
 from sklearn.metrics import roc_auc_score,f1_score,accuracy_score,precision_score,recall_score
 from scipy.sparse import csgraph,coo_matrix
-from Evaluation import Metric
+from GraphSL.Evaluation import Metric
 
 class LPSI:
     """

diff --git a/data/cora_ml → GraphSL/data/cora_ml b/data/cora_ml → GraphSL/data/cora_ml
diff --git a/data/digg16000 → GraphSL/data/digg16000 b/data/digg16000 → GraphSL/data/digg16000
diff --git a/data/dolphins → GraphSL/data/dolphins b/data/dolphins → GraphSL/data/dolphins
diff --git a/data/jazz → GraphSL/data/jazz b/data/jazz → GraphSL/data/jazz
diff --git a/data/karate → GraphSL/data/karate b/data/karate → GraphSL/data/karate
diff --git a/data/meme8000 → GraphSL/data/meme8000 b/data/meme8000 → GraphSL/data/meme8000
diff --git a/data/netscience → GraphSL/data/netscience b/data/netscience → GraphSL/data/netscience
diff --git a/data/power_grid → GraphSL/data/power_grid b/data/power_grid → GraphSL/data/power_grid
diff --git a/data/utils.py → GraphSL/data/utils.py b/data/utils.py → GraphSL/data/utils.py
@@ -7,7 +7,7 @@
 import copy
 
 
-def load_dataset(dataset, data_dir='data'):
+def load_dataset(dataset, data_dir='GraphSL\data'):
     """
     Load a dataset from a pickle file.
 

diff --git a/all.py b/all.py
diff --git a/dist/GraphSL-0.6.tar.gz b/dist/GraphSL-0.6.tar.gz
diff --git a/dist/GraphSL-0.7.tar.gz b/dist/GraphSL-0.7.tar.gz
diff --git a/setup.py b/setup.py
@@ -1,15 +1,15 @@
-from distutils.core import setup
+from setuptools import setup, find_packages
 
 setup(
   name = 'GraphSL',         # How you named your package folder (MyLib)
-  packages = ['GraphSL','GraphSL.data','GraphSL.Evaluation','GraphSL.GNN','GraphSL.Prescribed'],   # Chose the same as "name"
-  version = '0.6',      # Start with a small number and increase it with every change you make
+  packages = find_packages(),   # Chose the same as "name"
+  version = '0.7',      # Start with a small number and increase it with every change you make
   license='MIT',        # Chose a license from here: https://help.github.com/articles/licensing-a-repository
   description = 'Graph Source Localization Approaches and Benchmark Datasets',   # Give a short description about your library
   author = 'Junxiang Wang',                   # Type in your name
   author_email = 'junxiang.wang@alumni.emory.edu',      # Type in your E-Mail
   url = 'https://xianggebenben.github.io/Junxiang_Wang.github.io/',   # Provide either the link to your github or to your website
-  download_url = 'https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.6.tar.gz',    # I explain this later on
+  download_url = 'https://github.com/xianggebenben/GraphSL/archive/refs/tags/v.0.7.tar.gz',    # I explain this later on
   keywords = ['Graph Diffusion', 'Graph Source Localization', 'Prescribed Methods', 'GNN Methods','Benchmark'],   # Keywords that define your package best
   install_requires=[            # I get to this in a second
           'numpy',
@@ -26,4 +26,5 @@
     'Programming Language :: Python :: 3',      #Specify which pyhton versions that you want to support
     'Programming Language :: Python :: 3.10'
   ],
+python_requires=">=3.9",
 )