From 83daa3d902b164d83c25faa4e7ad9246831d7690 Mon Sep 17 00:00:00 2001
From: Utaemon Toyota <toyota@cl.uni-heidelberg.de>
Date: Wed, 27 Feb 2019 11:05:21 +0100
Subject: [PATCH] update README

---
 EP/Cora_node_classification/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/EP/Cora_node_classification/README.md b/EP/Cora_node_classification/README.md
index adfa8e5..2825825 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
-- 
GitLab