adding first two models

GraphSageCode/data_preprocess_gnn.py

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+import torch
+import torch_geometric
+from torch_geometric.data import Data, Dataset, InMemoryDataset
+import numpy as np
+import argparse
+
+
+
+def read_edge_index():
+    with open(egde_index_file) as f:
+        edges_str = f.read().splitlines()
+        edge_int = []
+        for edge in edges_str:
+            node = edge.split(" ")
+            edge_int.append(np.array(node, dtype=np.int32))
+        np_edges = np.array(edge_int)
+    return np.transpose(np_edges)
+map_label_to_index = {'hateful': 2, 'normal': 0, 'other':1}
+def read_node_feature_file():
+    with open(edge_feature_file) as f:
+        feature_line = f.read().splitlines()
+        features = []
+        y = []
+        for feature in feature_line:
+            feature = feature.split("\t")
+            features.append(np.array(feature[1:-1], dtype=np.float32))
+            y.append(map_label_to_index[feature[-1]])
+        return np.array(y), np.array(features)
+
+class RetweetDataset(InMemoryDataset):
+    def __init__(self, root_path, transform=None, pre_transform=None, feature_type='glove'):
+        super(RetweetDataset, self).__init__(root_path, transform, pre_transform)
+        data_index = 0 if feature_type == 'glove' else 1
+        self.data, self.slices = torch.load(self.processed_paths[data_index])
+
+    @property
+    def raw_file_names(self):
+        return []
+    @property
+    def processed_file_names(self):
+        return ['../data/input/processed_hate_glove_data.dataset', '../data/input/processed_hate_all_data.dataset']
+
+    def download(self):
+        pass
+
+    def process(self):
+        #size: (2, E)
+        np_edge_index = read_edge_index()
+        #size (num_node, 1) (num_node, feature vector size)
+        datalist = []
+        y, np_node_features = read_node_feature_file()
+        torch_edge_index = torch.LongTensor(np_edge_index)
+        torch_node_features = torch.FloatTensor(np_node_features)
+        torch_y = torch.LongTensor(y)
+        data_list = [Data(x=torch_node_features, edge_index = torch_edge_index, y=torch_y)]
+        data, slices = self.collate(data_list)
+        index = 0 if args.feature == 'glove' else 1
+        torch.save((data, slices), self.processed_paths[index])
+
+def construct_dataset(feature='glove'):
+    return RetweetDataset("../", feature_type=feature)
+
+def get_labeled_index(user_type='hate', feature_type='glove'):
+    edge_feature_file = '../data/users_' + user_type + '_' + feature_type +'.content'
+    labeled_hate_index = []
+    labeled_normal_index = []
+    with open(edge_feature_file) as f:
+        feature_line = f.read().splitlines()
+
+        for index, feature in enumerate(feature_line):
+            feature = feature.split("\t")
+            if (map_label_to_index[feature[-1]] == 2):
+                labeled_hate_index.append(index)
+            elif (map_label_to_index[feature[-1]] == 0):
+                labeled_normal_index.append(index)
+    return labeled_hate_index, labeled_normal_index
+
+
+
+if __name__ == "__main__":
+    #process & create the dataset files
+
+    parser = argparse.ArgumentParser()
+
+    # system
+    parser.add_argument("--feature", type=str, default="glove", help="glove | all")
+    #no use of user_type for now
+    parser.add_argument("--user_type", type=str, default="hate", help="hate | suspend")
+    args = parser.parse_args()
+
+    egde_index_file = '../data/users.edges'
+    edge_feature_file = '../data/users_' + args.user_type + '_' + args.feature +'.content'
+    dataset = RetweetDataset("../", feature_type=args.feature)
+    print(dataset[0].num_nodes)

models/gcn/__init__.py

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+from __future__ import print_function
+from __future__ import division