Add comprehensive NetworkX tutorials to exercise notebooks

ex-1-2.ipynb

@@ -54,6 +54,82 @@
     "# Part 1: Reading graphs from files / writing graphs to files + graph basics"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetworkX Tutorial: Graph I/O and Basics\n",
+    "\n",
+    "Before starting the exercises, let's review the key NetworkX concepts and functions you'll need.\n",
+    "\n",
+    "### Reading and Writing Graphs\n",
+    "\n",
+    "NetworkX supports various graph file formats:\n",
+    "\n",
+    "**Reading graphs:**\n",
+    "```python\n",
+    "# Read edge list formats\n",
+    "G = nx.read_edgelist('file.txt')           # Simple edge list\n",
+    "G = nx.read_weighted_edgelist('file.txt')  # With weights\n",
+    "\n",
+    "# Read other formats\n",
+    "G = nx.read_pajek('file.net')     # Pajek format\n",
+    "G = nx.read_gml('file.gml')       # GML format\n",
+    "G = nx.read_graphml('file.graphml') # GraphML format\n",
+    "```\n",
+    "\n",
+    "**Writing graphs:**\n",
+    "```python\n",
+    "nx.write_edgelist(G, 'output.txt')\n",
+    "nx.write_pajek(G, 'output.net')\n",
+    "nx.write_gml(G, 'output.gml')\n",
+    "```\n",
+    "\n",
+    "### Graph Types\n",
+    "\n",
+    "- `nx.Graph()` - Undirected graph\n",
+    "- `nx.DiGraph()` - Directed graph\n",
+    "- `nx.MultiGraph()` - Undirected multigraph (multiple edges between nodes)\n",
+    "- `nx.MultiDiGraph()` - Directed multigraph\n",
+    "\n",
+    "### Basic Graph Operations\n",
+    "\n",
+    "```python\n",
+    "# Graph information\n",
+    "G.number_of_nodes()  # Count nodes\n",
+    "G.number_of_edges()  # Count edges\n",
+    "G.nodes()            # Get all nodes\n",
+    "G.edges()            # Get all edges\n",
+    "\n",
+    "# Adding/removing nodes and edges\n",
+    "G.add_node(1)\n",
+    "G.add_edge(1, 2)\n",
+    "G.remove_node(1)\n",
+    "G.remove_edge(1, 2)\n",
+    "\n",
+    "# Checking existence\n",
+    "G.has_node(1)\n",
+    "G.has_edge(1, 2)\n",
+    "\n",
+    "# Graph comparison\n",
+    "nx.is_isomorphic(G1, G2)  # Check if graphs are structurally identical\n",
+    "```\n",
+    "\n",
+    "### Directed vs Undirected Graphs\n",
+    "\n",
+    "When reading a graph as directed vs undirected:\n",
+    "- **Undirected**: Each edge (u,v) is bidirectional\n",
+    "- **Directed**: Each edge has a specific direction from u to v\n",
+    "- Converting directed to undirected typically doubles edge count (each directed edge becomes one undirected edge)\n",
+    "\n",
+    "**\ud83d\udcda References:**\n",
+    "- [NetworkX I/O Documentation](https://networkx.org/documentation/stable/reference/readwrite/index.html)\n",
+    "- [NetworkX Graph Types](https://networkx.org/documentation/stable/reference/classes/index.html)\n",
+    "- [NetworkX Tutorial](https://networkx.org/documentation/stable/tutorial.html)\n",
+    "\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -235,6 +311,99 @@
     "# Part 2: Connected components, Giant Component & Subgraphs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NetworkX Tutorial: Connected Components and Subgraphs\n",
+    "\n",
+    "### Connected Components\n",
+    "\n",
+    "A connected component is a maximal set of nodes where every node is reachable from every other node.\n",
+    "\n",
+    "**For undirected graphs:**\n",
+    "```python\n",
+    "# Get all connected components\n",
+    "components = nx.connected_components(G)  # Returns generator of sets\n",
+    "component_list = list(nx.connected_components(G))\n",
+    "\n",
+    "# Number of connected components\n",
+    "num_components = nx.number_connected_components(G)\n",
+    "\n",
+    "# Get the largest connected component (Giant Component)\n",
+    "largest_cc = max(nx.connected_components(G), key=len)\n",
+    "giant_component = G.subgraph(largest_cc).copy()\n",
+    "\n",
+    "# Check if graph is connected\n",
+    "nx.is_connected(G)\n",
+    "```\n",
+    "\n",
+    "**For directed graphs:**\n",
+    "```python\n",
+    "# Strongly connected components (path in both directions)\n",
+    "scc = nx.strongly_connected_components(G)\n",
+    "\n",
+    "# Weakly connected components (treating edges as undirected)\n",
+    "wcc = nx.weakly_connected_components(G)\n",
+    "```\n",
+    "\n",
+    "### Subgraphs\n",
+    "\n",
+    "```python\n",
+    "# Create a subgraph from a set of nodes\n",
+    "nodes_subset = [1, 2, 3, 4, 5]\n",
+    "subG = G.subgraph(nodes_subset)  # Returns a view\n",
+    "subG_copy = G.subgraph(nodes_subset).copy()  # Create independent copy\n",
+    "\n",
+    "# Induced subgraph (includes all edges between nodes in subset)\n",
+    "induced_subG = G.subgraph(nodes_subset)\n",
+    "\n",
+    "# Edge-based subgraph\n",
+    "edges_subset = [(1,2), (2,3)]\n",
+    "edge_subG = G.edge_subgraph(edges_subset)\n",
+    "```\n",
+    "\n",
+    "### Giant Component\n",
+    "\n",
+    "The giant component is the largest connected component in a graph. It's particularly important in network analysis:\n",
+    "\n",
+    "```python\n",
+    "# Extract giant component\n",
+    "largest_cc = max(nx.connected_components(G), key=len)\n",
+    "giant = G.subgraph(largest_cc).copy()\n",
+    "\n",
+    "# Size comparison\n",
+    "print(f\"Original graph: {G.number_of_nodes()} nodes\")\n",
+    "print(f\"Giant component: {giant.number_of_nodes()} nodes\")\n",
+    "print(f\"Fraction in giant: {giant.number_of_nodes()/G.number_of_nodes():.2%}\")\n",
+    "```\n",
+    "\n",
+    "### Network Resilience\n",
+    "\n",
+    "Studying how networks respond to node/edge removal:\n",
+    "\n",
+    "```python\n",
+    "# Random failures - remove random nodes\n",
+    "import random\n",
+    "nodes_to_remove = random.sample(list(G.nodes()), k=10)\n",
+    "G_failed = G.copy()\n",
+    "G_failed.remove_nodes_from(nodes_to_remove)\n",
+    "\n",
+    "# Targeted attacks - remove high-degree nodes\n",
+    "degree_sorted = sorted(G.degree(), key=lambda x: x[1], reverse=True)\n",
+    "top_nodes = [node for node, deg in degree_sorted[:10]]\n",
+    "G_attacked = G.copy()\n",
+    "G_attacked.remove_nodes_from(top_nodes)\n",
+    "```\n",
+    "\n",
+    "**\ud83d\udcda References:**\n",
+    "- [NetworkX Components](https://networkx.org/documentation/stable/reference/algorithms/component.html)\n",
+    "- [NetworkX Subgraphs](https://networkx.org/documentation/stable/reference/classes/generated/networkx.Graph.subgraph.html)\n",
+    "- [Network Resilience Analysis](https://networkx.org/documentation/stable/reference/algorithms/connectivity.html)\n",
+    "\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "94a45b3e",
@@ -377,4 +546,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}