Editing Graph Metrics (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Modeling 'The Social Influencer' (Calculating who is the 'Center' of a group):'''
<syntaxhighlight lang="python">
def calculate_influence(adjacency_list):
    """
    Shows how 'Degree' determines immediate reach.
    """
    scores = {}
    for person, friends in adjacency_list.items():
        # Degree Centrality = Count of friends
        scores[person] = len(friends)
        
    # Sort to find the top influencer
    ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)
    return ranked

# Network: Alice knows (Bob, Charlie), Bob knows (Alice, Dan), Charlie knows (Alice).
network = {
    "Alice": ["Bob", "Charlie"],
    "Bob": ["Alice", "Dan"],
    "Charlie": ["Alice"],
    "Dan": ["Bob"]
}

print(f"Influence Ranking: {calculate_influence(network)}")
</syntaxhighlight>

; Metric Landmarks
: '''The 'Königsberg Bridges' (Euler, 1736)''' → The "Birth of Graph Theory." Leonhard Euler proved that it was "Impossible" to walk through the city and cross every bridge once, starting the science of "Nodes and Edges."
: '''Google PageRank''' → The most profitable graph metric in history. It uses "Eigenvector Centrality" to decide which websites are "Important" based on who "Links" to them.
: '''The 'Strength of Weak Ties' (Granovetter)''' → The discovery that your "Acquaintances" (Weak Ties) are more likely to find you a "Job" than your "Close Friends" because they connect you to "New parts of the network."
: '''Epidemic Modeling''' → Using graph metrics to find the "Super-spreaders" of a virus (Nodes with high degree and high betweenness) to "Vaccinate" them first and "Break" the network.
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