Update ReadMe.txt

Author: smty2018

GNN for Node Classification/ReadMe.txt

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 Description:
 a Graph Neural Network (GNN) for node classification on graph-structured data.
- The example uses the PyTorch Geometric library to build and train a GNN on the Cora dataset.
+ It uses the PyTorch Geometric library to build and train a GNN on the Cora dataset.
 
-Graph Neural Networks (GNNs) are designed to operate on graph-structured data, where nodes represent entities and edges represent relationships between entities. This project showcases a simplified GNN architecture for node classification, helping you understand the fundamentals of GNNs and their application in real-world scenarios.
+Graph Neural Networks (GNNs) are designed to operate on graph-structured data, where nodes represent entities and edges represent relationships between entities. 
 
 Dataset: The code uses the Cora dataset, which is a well-known citation network dataset. It consists of scientific publications as nodes in a graph, where edges represent citation relationships between publications. Each node (publication) has a bag-of-words feature representation and a class label indicating its research field.