add: intrductions and changed adjacency matrix from lower to upper

doc/source/community_detection_guide/notebooks/consensus_clustering.ipynb

@@ -7,7 +7,19 @@
    "source": [
     "# Consensus clustering\n",
     "\n",
-    "Consensus clustering is a method that combines the results of multiple clustering runs to produce a more stable and reliable clustering solution. It is particularly useful when the results of a single clustering algorithm are not robust or consistent across different runs. By aggregating the outputs of various clustering runs, consensus clustering aims to identify clusters that are consistently found across different runs and are therefore more likely to be true representations of the underlying data structur"
+    "Consensus clustering is a powerful technique for overcoming the instability of single community detection runs. While a single algorithm might give you one partition of a network, running it multiple times can often produce different results, leaving you unsure of which outcome is the most reliable.\n",
+    "\n",
+    "This notebook introduces the concept of **consensus clustering**, a method that combines the results of multiple community detection runs to produce a more stable and robust community structure. It aims to identify communities that are consistently found across different runs and are therefore more likely to be true representations of the underlying data structure.\n",
+    "\n",
+    "We will learn how to:\n",
+    "\n",
+    "* Run a community detection algorithm multiple times on the same network.\n",
+    "\n",
+    "* Construct a **co-occurrence matrix** to track how often nodes are clustered together.\n",
+    "\n",
+    "* Use this matrix to find a final, more stable consensus partition.\n",
+    "\n",
+    "---"
    ]
   },
   {