diff --git a/data/karate/karate.cites b/data/karate/karate.cites
new file mode 100644
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diff --git a/data/karate/karate.content b/data/karate/karate.content
new file mode 100644
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diff --git a/data/karate/karate.gml b/data/karate/karate.gml
new file mode 100644
index 0000000..ecafe9c
--- /dev/null
+++ b/data/karate/karate.gml
@@ -0,0 +1,530 @@
+Creator "Mark Newman on Fri Jul 21 12:39:27 2006"
+graph
+[
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diff --git a/data/karate/karate.txt b/data/karate/karate.txt
new file mode 100644
index 0000000..9569ed5
--- /dev/null
+++ b/data/karate/karate.txt
@@ -0,0 +1,5 @@
+The file karate.gml contains the network of friendships between the 34
+members of a karate club at a US university, as described by Wayne Zachary
+in 1977.  If you use these data in your work, please cite W. W. Zachary, An
+information flow model for conflict and fission in small groups, Journal of
+Anthropological Research 33, 452-473 (1977).
diff --git a/data/karate/karate.zip b/data/karate/karate.zip
new file mode 100644
index 0000000..38d1083
Binary files /dev/null and b/data/karate/karate.zip differ
diff --git a/data/karate/test_gml.py b/data/karate/test_gml.py
new file mode 100644
index 0000000..5f5c2f5
--- /dev/null
+++ b/data/karate/test_gml.py
@@ -0,0 +1,19 @@
+import networkx as nx
+import matplotlib.pyplot as plt
+G=nx.karate_club_graph()
+nx.draw(G, with_labels = True)
+plt.show()
+
+file = open('karate.cites','w')
+for i in G.edges():
+    file.write(str(i[0])+' '+str(i[1])+'\n')
+file.close()
+file = open('karate.content','w')
+for i in G.nodes():
+    file.write(str(i))
+    temp = [0 for j in range(34)]
+    temp[i] = 1
+    for j in temp:
+	file.write(" "+str(j))
+    file.write(' 1\n')
+file.close()
diff --git a/img/compare.png b/img/compare.png
new file mode 100644
index 0000000..eec768d
Binary files /dev/null and b/img/compare.png differ
diff --git a/pygcn/models2.py b/pygcn/models2.py
new file mode 100644
index 0000000..1a68d88
--- /dev/null
+++ b/pygcn/models2.py
@@ -0,0 +1,22 @@
+import torch.nn as nn
+import torch.nn.functional as F
+from layers import GraphConvolution
+
+
+class GCN(nn.Module):
+    def __init__(self, nfeat, nhid1, nhid2, nclass, dropout):
+        super(GCN, self).__init__()
+
+        self.gc1 = GraphConvolution(nfeat, nhid1)
+        self.gc2 = GraphConvolution(nhid1, nhid2)
+        self.gc3 = GraphConvolution(nhid2, nclass)
+        self.dropout = dropout
+
+    def forward(self, x, adj):
+        x = F.tanh(self.gc1(x, adj))
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = F.tanh(self.gc2(x, adj))
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.gc3(x, adj)
+        
+        return F.softmax(x) 
diff --git a/pygcn/train_karate.py b/pygcn/train_karate.py
new file mode 100644
index 0000000..bf9bd6b
--- /dev/null
+++ b/pygcn/train_karate.py
@@ -0,0 +1,146 @@
+from __future__ import division
+from __future__ import print_function
+
+import time
+import argparse
+import numpy as np
+
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+import torch.optim as optim
+
+from utils import load_data, accuracy, encode_onehot
+from models2 import GCN
+
+# Training settings
+parser = argparse.ArgumentParser()
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='Disables CUDA training.')
+parser.add_argument('--fastmode', action='store_true', default=False,
+                    help='Validate during training pass.')
+parser.add_argument('--seed', type=int, default=42, help='Random seed.')
+parser.add_argument('--epochs', type=int, default=200,
+                    help='Number of epochs to train.')
+parser.add_argument('--lr', type=float, default=0.01,
+                    help='Initial learning rate.')
+parser.add_argument('--weight_decay', type=float, default=5e-4,
+                    help='Weight decay (L2 loss on parameters).')
+parser.add_argument('--hidden1', type=int, default=200,
+                    help='Number of hidden units.')
+parser.add_argument('--hidden2', type=int, default=50,
+                    help='Number of hidden units.')
+parser.add_argument('--dropout', type=float, default=0.5,
+                    help='Dropout rate (1 - keep probability).')
+
+args = parser.parse_args()
+args.cuda = not args.no_cuda and torch.cuda.is_available()
+
+np.random.seed(args.seed)
+torch.manual_seed(args.seed)
+if args.cuda:
+    torch.cuda.manual_seed(args.seed)
+
+# Load data
+adj, features, labels, idx_train, idx_val, idx_test = load_data("../data/karate/","karate")
+# adj, features, labels, idx_train, idx_val, idx_test = load_data("../data/cora/","cora")
+
+# Model and optimizer
+model = GCN(nfeat=features.shape[1],
+            nhid1=args.hidden1,
+	    nhid2=args.hidden2,
+            nclass=4,
+            dropout=args.dropout)
+
+optimizer = optim.Adam(model.parameters(),
+                       lr=args.lr, weight_decay=args.weight_decay)
+
+if args.cuda:
+    model.cuda()
+    features = features.cuda()
+    adj = adj.cuda()
+    labels = labels.cuda()
+    idx_train = idx_train.cuda()
+    idx_val = idx_val.cuda()
+    idx_test = idx_test.cuda()
+
+
+criterion = nn.CrossEntropyLoss()
+indices = torch.LongTensor([0, 16,18,24])
+
+def train(epoch):
+    t = time.time()
+    model.train()
+    optimizer.zero_grad()
+    output = model(features, adj)
+    #output2 = PCA(output.detach().numpy())[0]
+    #ax = plt.scatter(output2[:,0],output2[:,1])
+    #plt.show()
+    loss = criterion(torch.index_select(output, 0, indices),torch.LongTensor([0,1,2,3]))
+    loss.backward()
+    optimizer.step()
+
+    if not args.fastmode:
+        model.eval()
+        output = model(features, adj)
+
+    print('Epoch: {:04d}'.format(epoch+1),
+          'time: {:.4f}s'.format(time.time() - t))
+
+for epoch in range(args.epochs):
+    train(epoch)
+
+def PCA(data, dims_rescaled_data=2):
+    """
+    returns: data transformed in 2 dims/columns + regenerated original data
+    pass in: data as 2D NumPy array
+    """
+    import numpy as NP
+    from scipy import linalg as LA
+    m, n = data.shape
+    # mean center the data
+    data -= data.mean(axis=0)
+    # calculate the covariance matrix
+    R = NP.cov(data, rowvar=False)
+    # calculate eigenvectors & eigenvalues of the covariance matrix
+    # use 'eigh' rather than 'eig' since R is symmetric, 
+    # the performance gain is substantial
+    evals, evecs = LA.eigh(R)
+    # sort eigenvalue in decreasing order
+    idx = NP.argsort(evals)[::-1]
+    evecs = evecs[:,idx]
+    # sort eigenvectors according to same index
+    evals = evals[idx]
+    # select the first n eigenvectors (n is desired dimension
+    # of rescaled data array, or dims_rescaled_data)
+    evecs = evecs[:, :dims_rescaled_data]
+    # carry out the transformation on the data using eigenvectors
+    # and return the re-scaled data, eigenvalues, and eigenvectors
+    return NP.dot(evecs.T, data.T).T, evals, evecs
+
+output = model(features, adj)
+print (output)
+output = PCA(output.detach().numpy())[0]
+print (output)
+
+# draw
+import matplotlib.pyplot as plt
+plt.scatter(output[:,0],output[:,1])
+plt.show()
+
+# classify 
+community = dict()
+for index,i in enumerate(output):
+    temp = [np.linalg.norm(i-output[0,:]),
+             np.linalg.norm(i-output[16,:]),
+             np.linalg.norm(i-output[18,:]),
+             np.linalg.norm(i-output[24,:])]
+    belongs = temp.index(min(temp))
+    if belongs not in community.keys():
+    	community[belongs]=[]
+	community[belongs].append(index)
+    else:
+	community[belongs].append(index)
+print (community)
+#for i,_ in enumerate(output[:,0]):
+#    plt.annotate(i,(output[:,0][i],output[:,1][i]))