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+# NetworkX Graph Basics
+
+## Graph Types
+
+NetworkX supports four main graph classes:
+
+### Graph (Undirected)
+```python
+import networkx as nx
+G = nx.Graph()
+```
+- Undirected graphs with single edges between nodes
+- No parallel edges allowed
+- Edges are bidirectional
+
+### DiGraph (Directed)
+```python
+G = nx.DiGraph()
+```
+- Directed graphs with one-way connections
+- Edge direction matters: (u, v) ≠ (v, u)
+- Used for modeling directed relationships
+
+### MultiGraph (Undirected Multi-edge)
+```python
+G = nx.MultiGraph()
+```
+- Allows multiple edges between same node pairs
+- Useful for modeling multiple relationships
+
+### MultiDiGraph (Directed Multi-edge)
+```python
+G = nx.MultiDiGraph()
+```
+- Directed graph with multiple edges between nodes
+- Combines features of DiGraph and MultiGraph
+
+## Creating and Adding Nodes
+
+### Single Node Addition
+```python
+G.add_node(1)
+G.add_node("protein_A")
+G.add_node((x, y))  # Nodes can be any hashable type
+```
+
+### Bulk Node Addition
+```python
+G.add_nodes_from([2, 3, 4])
+G.add_nodes_from(range(100, 110))
+```
+
+### Nodes with Attributes
+```python
+G.add_node(1, time='5pm', color='red')
+G.add_nodes_from([
+    (4, {"color": "red"}),
+    (5, {"color": "blue", "weight": 1.5})
+])
+```
+
+### Important Node Properties
+- Nodes can be any hashable Python object: strings, tuples, numbers, custom objects
+- Node attributes stored as key-value pairs
+- Use meaningful node identifiers for clarity
+
+## Creating and Adding Edges
+
+### Single Edge Addition
+```python
+G.add_edge(1, 2)
+G.add_edge('gene_A', 'gene_B')
+```
+
+### Bulk Edge Addition
+```python
+G.add_edges_from([(1, 2), (1, 3), (2, 4)])
+G.add_edges_from(edge_list)
+```
+
+### Edges with Attributes
+```python
+G.add_edge(1, 2, weight=4.7, relation='interacts')
+G.add_edges_from([
+    (1, 2, {'weight': 4.7}),
+    (2, 3, {'weight': 8.2, 'color': 'blue'})
+])
+```
+
+### Adding from Edge List with Attributes
+```python
+# From pandas DataFrame
+import pandas as pd
+df = pd.DataFrame({'source': [1, 2], 'target': [2, 3], 'weight': [4.7, 8.2]})
+G = nx.from_pandas_edgelist(df, 'source', 'target', edge_attr='weight')
+```
+
+## Examining Graph Structure
+
+### Basic Properties
+```python
+# Get collections
+G.nodes              # NodeView of all nodes
+G.edges              # EdgeView of all edges
+G.adj                # AdjacencyView for neighbor relationships
+
+# Count elements
+G.number_of_nodes()  # Total node count
+G.number_of_edges()  # Total edge count
+len(G)              # Number of nodes (shorthand)
+
+# Degree information
+G.degree()          # DegreeView of all node degrees
+G.degree(1)         # Degree of specific node
+list(G.degree())    # List of (node, degree) pairs
+```
+
+### Checking Existence
+```python
+# Check if node exists
+1 in G              # Returns True/False
+G.has_node(1)
+
+# Check if edge exists
+G.has_edge(1, 2)
+```
+
+### Accessing Neighbors
+```python
+# Get neighbors of node 1
+list(G.neighbors(1))
+list(G[1])          # Dictionary-like access
+
+# For directed graphs
+list(G.predecessors(1))  # Incoming edges
+list(G.successors(1))    # Outgoing edges
+```
+
+### Iterating Over Elements
+```python
+# Iterate over nodes
+for node in G.nodes:
+    print(node, G.nodes[node])  # Access node attributes
+
+# Iterate over edges
+for u, v in G.edges:
+    print(u, v, G[u][v])  # Access edge attributes
+
+# Iterate with attributes
+for node, attrs in G.nodes(data=True):
+    print(node, attrs)
+
+for u, v, attrs in G.edges(data=True):
+    print(u, v, attrs)
+```
+
+## Modifying Graphs
+
+### Removing Elements
+```python
+# Remove single node (also removes incident edges)
+G.remove_node(1)
+
+# Remove multiple nodes
+G.remove_nodes_from([1, 2, 3])
+
+# Remove edges
+G.remove_edge(1, 2)
+G.remove_edges_from([(1, 2), (2, 3)])
+```
+
+### Clearing Graph
+```python
+G.clear()           # Remove all nodes and edges
+G.clear_edges()     # Remove only edges, keep nodes
+```
+
+## Attributes and Metadata
+
+### Graph-Level Attributes
+```python
+G.graph['name'] = 'Social Network'
+G.graph['date'] = '2025-01-15'
+print(G.graph)
+```
+
+### Node Attributes
+```python
+# Set at creation
+G.add_node(1, time='5pm', weight=0.5)
+
+# Set after creation
+G.nodes[1]['time'] = '6pm'
+nx.set_node_attributes(G, {1: 'red', 2: 'blue'}, 'color')
+
+# Get attributes
+G.nodes[1]
+G.nodes[1]['time']
+nx.get_node_attributes(G, 'color')
+```
+
+### Edge Attributes
+```python
+# Set at creation
+G.add_edge(1, 2, weight=4.7, color='red')
+
+# Set after creation
+G[1][2]['weight'] = 5.0
+nx.set_edge_attributes(G, {(1, 2): 10.5}, 'weight')
+
+# Get attributes
+G[1][2]
+G[1][2]['weight']
+G.edges[1, 2]
+nx.get_edge_attributes(G, 'weight')
+```
+
+## Subgraphs and Views
+
+### Subgraph Creation
+```python
+# Create subgraph from node list
+nodes_subset = [1, 2, 3, 4]
+H = G.subgraph(nodes_subset)  # Returns view (references original)
+
+# Create independent copy
+H = G.subgraph(nodes_subset).copy()
+
+# Edge-induced subgraph
+edge_subset = [(1, 2), (2, 3)]
+H = G.edge_subgraph(edge_subset)
+```
+
+### Graph Views
+```python
+# Reverse view (for directed graphs)
+G_reversed = G.reverse()
+
+# Convert between directed/undirected
+G_undirected = G.to_undirected()
+G_directed = G.to_directed()
+```
+
+## Graph Information and Diagnostics
+
+### Basic Information
+```python
+print(nx.info(G))   # Summary of graph structure
+
+# Density (ratio of actual edges to possible edges)
+nx.density(G)
+
+# Check if graph is directed
+G.is_directed()
+
+# Check if graph is multigraph
+G.is_multigraph()
+```
+
+### Connectivity Checks
+```python
+# For undirected graphs
+nx.is_connected(G)
+nx.number_connected_components(G)
+
+# For directed graphs
+nx.is_strongly_connected(G)
+nx.is_weakly_connected(G)
+```
+
+## Important Considerations
+
+### Floating Point Precision
+Once graphs contain floating point numbers, all results are inherently approximate due to precision limitations. Small arithmetic errors can affect algorithm outcomes, particularly in minimum/maximum computations.
+
+### Memory Considerations
+Each time a script starts, graph data must be loaded into memory. For large datasets, this can cause performance issues. Consider:
+- Using efficient data formats (pickle for Python objects)
+- Loading only necessary subgraphs
+- Using graph databases for very large networks
+
+### Node and Edge Removal Behavior
+When a node is removed, all edges incident with that node are automatically removed as well.