Node Metrics¶

Node metrics characterize individual nodes in the graph.

Available Metrics¶

Degree Information¶

g.degree[node]  # Total degree of node
# For directed graphs:
g.in_degree[node]  # In-degree
g.out_degree[node]  # Out-degree

Clustering¶

g.clustering_of(node)  # Local clustering coefficient
g.triangles_of(node)  # Number of triangles through node

Neighbors¶

g.neighbors(node)  # Adjacent nodes
# For directed graphs:
g.out_neighbors(node)  # Outgoing neighbors
g.in_neighbors(node)  # Incoming neighbors

Attributes¶

g.get_node_attr(node)  # Node attribute value
g.contains_node(node)  # Does node exist

Examples¶

import pygraphina as pg

# Create a graph
g = pg.PyGraph()
nodes = [g.add_node(i) for i in range(10)]

# Add some edges
for i in range(9):
    g.add_edge(nodes[i], nodes[i + 1], 1.0)
g.add_edge(nodes[0], nodes[5], 1.0)

# Analyze each node
for node in nodes:
    degree = g.degree[node]
    neighbors = g.neighbors(node)
    clustering = g.clustering_of(node)
    triangles = g.triangles_of(node)

    print(f"Node {node}:")
    print(f"  Degree: {degree}")
    print(f"  Neighbors: {neighbors}")
    print(f"  Clustering: {clustering:.3f}")
    print(f"  Triangles: {triangles}")

Interpretation¶

Degree: Number of connections (importance in many contexts)
Clustering: How densely connected the neighbors are
Triangles: Participation in cohesive groups
Neighbors: Direct connections

Time Complexity¶

Metric	Complexity
Degree	O(1)
Neighbors	O(degree)
Clustering	O(degree²)
Triangles	O(degree²)

Use Cases¶

Finding influential nodes (high degree)
Identifying cohesive groups (high clustering)
Community structure analysis
Network resilience (removing high-degree nodes)