Update GNN_Fuzzy.ipynb

GNN_Fuzzy.ipynb

@@ -1,5 +1,26 @@
 {
  "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Graph Neural Networks.\n",
+    "\n",
+    "    How do they work ?\n",
+    "\n",
+    "\n",
+    "Graph Neural Networks (GNNs) are a class of neural networks designed to operate on graph-structured data. They are used to analyze and make predictions about graphs, such as social networks, protein networks, and transportation networks.\n",
+    "\n",
+    "A GNN typically consists of an encoder that maps the graph's nodes and edges to a low-dimensional representation, and a decoder that maps the low-dimensional representation back to the graph's nodes and edges. The encoder and decoder are connected by a series of layers, each of which performs a different operation on the graph's nodes and edges.\n",
+    "\n",
+    "The key feature of GNNs is that they can take into account the neighborhood structure of the graph, meaning the connections between nodes. This is done by using message passing mechanisms to propagate information between nodes. In each layer of the GNN, each node updates its representation by aggregating information from its neighboring nodes.\n",
+    "\n",
+    "There are different variants of GNNs, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), which use different techniques for message passing and aggregation. GCNs use convolutional operations to propagate information between nodes, while GATs use attention mechanisms to weight the importance of different neighbors.\n",
+    "\n",
+    "In summary, GNNs use message passing and aggregation mechanisms to incorporate the neighborhood structure of a graph into the neural network's computation, allowing it to make predictions about graph-structured data."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -132,6 +153,11 @@
     "\n",
     "print('Training complete')\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {