diff --git a/EP/Cora_node_classification/README.md b/EP/Cora_node_classification/README.md
index adfa8e5867a984d3ba8e160eb771ff765c568818..2825825b94f1e82128882a7a21143d2cd1324b4d 100644
--- a/EP/Cora_node_classification/README.md
+++ b/EP/Cora_node_classification/README.md
@@ -7,7 +7,7 @@ Betreuerin: Prof. Dr. Anette Frank
 Graph Embedding Propagation
 
 # Cora Node Classification
-To evaluate the trained graph and the embeddings the task of node classification will be executed. First, the data of cora will be imported into a networkX graph, which will be saved in a pickle file to use it for the training of the embeddings with our EP-SP algorithm. Afterwards the trained embedding will be evaluated with LibLinear L2-Logistic Regression provided from sklearn.
+To evaluate the trained graph and the embeddings the task of node classification will be executed. First, the data of cora will be imported into a networkX graph, which will be saved in a pickle file to use it for the training of the embeddings with our EP-SP algorithm. Afterwards the trained embeddings will be evaluated with LibLinear L2-Logistic Regression provided from sklearn over a transductive setting with 1000 random nodes for validation, 1000 random nodes for testing and 20 random nodes per class for training. For each iteration where the sets are newly splitted the random seed is set to to the iteration number.
 Graph building is provided on cora.py, the evaluation on node_classification.py.
 
 # Required Data