diff --git a/src/decagon_pytorch/data/trainprep.py b/src/decagon_pytorch/data/trainprep.py
index 18373ea..b2b0a49 100644
--- a/src/decagon_pytorch/data/trainprep.py
+++ b/src/decagon_pytorch/data/trainprep.py
@@ -1,2 +1,77 @@
-def trainprep(data):
-    pass
+from .sampling import fixed_unigram_candidate_sampler
+import torch
+
+
+def train_val_test_split_edges(edges, ratios):
+    train_ratio, val_ratio, test_ratio = ratios
+
+    if train_ratio + val_ratio + test_ratio != 1.0:
+        raise ValueError('Train, validation and test ratios must add up to 1')
+
+    order = torch.randperm(len(edges))
+    edges = edges[order, :]
+    n = round(len(edges) * train_ratio)
+    edges_train = edges[:n]
+    n_1 = round(len(edges) * (train_ratio + val_ratio))
+    edges_val = edges[n:n_1]
+    edges_test = edges[n_1:]
+
+    return edges_train, edges_val, edges_test
+
+
+def prepare_adj_mat(adj_mat, ratios):
+    degrees = adj_mat.sum(0)
+    edges_pos = torch.nonzero(adj_mat)
+
+    neg_neighbors = fixed_unigram_candidate_sampler(edges_pos[:, 1],
+        len(edges), degrees, 0.75)
+    edges_neg = torch.cat((edges_pos[:, 0], neg_neighbors.view(-1, 1)), 1)
+
+    edges_pos = (edges_pos_train, edges_pos_val, edges_pos_test) = \
+        train_val_test_split_edges(edges_pos, ratios)
+    edges_neg = (edges_neg_train, edges_neg_val, edges_neg_test) = \
+        train_val_test_split_edges(edges_neg, ratios)
+
+    return edges_pos, edges_neg
+
+
+class PreparedRelation(object):
+    def __init__(self, node_type_row, node_type_column,
+        adj_mat_train, adj_mat_train_trans,
+        edges_pos, edges_neg, edges_pos_trans, edges_neg_trans):
+
+        self.adj_mat_train = adj_mat_train
+        self.adj_mat_train_trans = adj_mat_train_trans
+        self.edges_pos = edges_pos
+        self.edges_neg = edges_neg
+        self.edges_pos_trans = edges_pos_trans
+        self.edges_neg_trans = edges_neg_trans
+
+
+def prepare_relation(r, ratios):
+    adj_mat = r.get_adjacency_matrix(r.node_type_row, r.node_type_column)
+    edges_pos, edges_neg = prepare_adj_mat(adj_mat)
+
+    # adj_mat_train = torch.zeros_like(adj_mat)
+    # adj_mat_train[edges_pos[0][:, 0], edges_pos[0][:, 0]] = 1
+    adj_mat_train = torch.sparse_coo_tensor(indices = edges_pos[0].transpose(0, 1),
+        values=torch.ones(len(edges_pos[0]), dtype=adj_mat.dtype))
+
+    if r.node_type_row != r.node_type_col:
+        adj_mat_trans = r.get_adjacency_matrix(r.node_type_col, r.node_type_row)
+        edges_pos_trans, edges_neg_trans = prepare_adj_mat(adj_mat_trans)
+        adj_mat_train_trans = torch.sparse_coo_tensor(indices = edges_pos_trans[0].transpose(0, 1),
+            values=torch.ones(len(edges_pos_trans[0]), dtype=adj_mat_trans.dtype))
+    else:
+        adj_mat_train_trans = adj_mat_trans = \
+            edge_pos_trans = edge_neg_trans = None
+
+    return PreparedRelation(r.node_type_row, r.node_type_column,
+        adj_mat_train, adj_mat_trans_train,
+        edges_pos, edges_neg, edges_pos_trans, edges_neg_trans)
+
+
+def prepare_training(data):
+    for (node_type_row, node_type_column), rels in data.relation_types:
+        for r in rels:
+            prep_relation_edges()
diff --git a/src/decagon_pytorch/normalize.py b/src/decagon_pytorch/normalize.py
index 7eb7150..a1af15c 100644
--- a/src/decagon_pytorch/normalize.py
+++ b/src/decagon_pytorch/normalize.py
@@ -33,3 +33,24 @@ def normalize_adjacency_matrix(adj):
         coldegree_mat_inv = sp.diags(np.nan_to_num(np.power(colsum, -0.5)).flatten())
         adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo()
     return sparse_to_tuple(adj_normalized)
+
+
+def norm_adj_mat_one_node_type(adj):
+    adj = sp.coo_matrix(adj)
+    assert adj.shape[0] == adj.shape[1]:
+    adj_ = adj + sp.eye(adj.shape[0])
+    rowsum = np.array(adj_.sum(1))
+    degree_mat_inv_sqrt = np.power(rowsum, -0.5).flatten()
+    degree_mat_inv_sqrt = sp.diags(degree_mat_inv_sqrt)
+    adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt)
+    return adj_normalized
+
+
+def norm_adj_mat_two_node_types(adj):
+    adj = sp.coo_matrix(adj)
+    rowsum = np.array(adj.sum(1))
+    colsum = np.array(adj.sum(0))
+    rowdegree_mat_inv = sp.diags(np.nan_to_num(np.power(rowsum, -0.5)).flatten())
+    coldegree_mat_inv = sp.diags(np.nan_to_num(np.power(colsum, -0.5)).flatten())
+    adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo()
+    return adj_normalized