--- a
+++ b/HINT/gnn_layers.py
@@ -0,0 +1,109 @@
+import math
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+from torch.nn.modules.module import Module
+torch.manual_seed(3) 
+np.random.seed(1)
+
+class GraphConvolution(Module):
+    """
+    Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
+    """
+
+    def __init__(self, in_features, out_features, bias=True, init='xavier'):
+        super(GraphConvolution, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
+        if bias:
+            self.bias = Parameter(torch.FloatTensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        if init == 'uniform':
+            print("| Uniform Initialization")
+            self.reset_parameters_uniform()
+        elif init == 'xavier':
+            print("| Xavier Initialization")
+            self.reset_parameters_xavier()
+        elif init == 'kaiming':
+            print("| Kaiming Initialization")
+            self.reset_parameters_kaiming()
+        else:
+            raise NotImplementedError
+
+    def reset_parameters_uniform(self):
+        stdv = 1. / math.sqrt(self.weight.size(1))
+        self.weight.data.uniform_(-stdv, stdv)
+        if self.bias is not None:
+            self.bias.data.uniform_(-stdv, stdv)
+
+    def reset_parameters_xavier(self):
+        nn.init.xavier_normal_(self.weight.data, gain=0.02) # Implement Xavier Uniform
+        if self.bias is not None:
+            nn.init.constant_(self.bias.data, 0.0)
+
+    def reset_parameters_kaiming(self):
+        nn.init.kaiming_normal_(self.weight.data, a=0, mode='fan_in')
+        if self.bias is not None:
+            nn.init.constant_(self.bias.data, 0.0)
+
+    def forward(self, input, adj):
+        support = torch.mm(input, self.weight)
+        output = torch.spmm(adj, support)
+        if self.bias is not None:
+            return output + self.bias
+        else:
+            return output
+
+    def __repr__(self):
+        return self.__class__.__name__ + ' (' \
+               + str(self.in_features) + ' -> ' \
+               + str(self.out_features) + ')'
+
+
+class GraphAttention(nn.Module):
+    """
+    Simple GAT layer, similar to https://arxiv.org/abs/1710.10903
+    """
+
+    def __init__(self, in_features, out_features, dropout, alpha, concat=True):
+        super(GraphAttention, self).__init__()
+        self.dropout = dropout
+        self.in_features = in_features
+        self.out_features = out_features
+        self.alpha = alpha
+        self.concat = concat
+
+        self.W = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(in_features, out_features).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True)
+        self.a1 = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(out_features, 1).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True)
+        self.a2 = nn.Parameter(nn.init.xavier_normal_(torch.Tensor(out_features, 1).type(torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor), gain=np.sqrt(2.0)), requires_grad=True)
+
+        self.leakyrelu = nn.LeakyReLU(self.alpha)
+
+    def forward(self, input, adj):
+        h = torch.mm(input, self.W)
+        N = h.size()[0]
+
+        f_1 = torch.matmul(h, self.a1)
+        f_2 = torch.matmul(h, self.a2)
+        e = self.leakyrelu(f_1 + f_2.transpose(0,1))
+
+        zero_vec = -9e15*torch.ones_like(e)
+        attention = torch.where(adj > 0, e, zero_vec)
+        attention = F.softmax(attention, dim=1)
+        attention = F.dropout(attention, self.dropout, training=self.training)
+        h_prime = torch.matmul(attention, h)
+
+        if self.concat:
+            return F.elu(h_prime)
+        else:
+            return h_prime
+
+    def __repr__(self):
+        return self.__class__.__name__ + ' (' + str(self.in_features) + ' -> ' + str(self.out_features) + ')'
+
+
+