Graph Metrics

Graph metrics provide summary statistics about the overall structure of a network. These help understand properties like how tightly connected the graph is, how clustered it is, and how node degrees are distributed.

Setup

create table edges as
select *
from (values (1::bigint, 2::bigint),
             (1, 3),
             (2, 3),
             (2, 4),
             (3, 4),
             (3, 5),
             (4, 5),
             (5, 6)) t(src, dst);

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Diameter

The diameter is the longest shortest path between any two nodes. It measures how "spread out" the graph is. A small diameter means information can travel quickly across the network.

select diameter
from onager_mtr_diameter((select src, dst from edges));

Column	Type	Description
diameter	bigint	Maximum shortest path in the graph

---

Radius

The radius is the minimum eccentricity of any node (the smallest maximum distance from any node to all other nodes). Nodes achieving the radius are called center nodes.

select radius
from onager_mtr_radius((select src, dst from edges));

Column	Type	Description
radius	bigint	Minimum eccentricity in the graph

---

Average Clustering Coefficient

Measures how much nodes tend to cluster together. A clustering coefficient of 1.0 means every node's neighbors are all connected to each other (perfect cliques).

\[ C_i = \frac{2 \cdot |\{e_{jk}\}|}{k_i(k_i - 1)} \]

where \(k_i\) is the degree of node \(i\) and \(|\{e_{jk}\}|\) is the number of edges between its neighbors.

select round(avg_clustering, 4) as clustering
from onager_mtr_avg_clustering((select src, dst from edges));

Column	Type	Description
avg_clustering	double	Average local clustering coefficient

---

Transitivity

Global clustering coefficient. The ratio of triangles to connected triples. Measures the overall tendency of the graph to form triangles.

\[ T = \frac{3 \times \text{triangles}}{\text{connected triples}} \]

select round(transitivity, 4) as transitivity
from onager_mtr_transitivity((select src, dst from edges));

Column	Type	Description
transitivity	double	Fraction of triangles to triples

---

Triangle Count

Counts the number of triangles each node participates in. Triangles indicate strong local connectivity and are the basis for clustering coefficients.

select node_id, triangles
from onager_mtr_triangles((select src, dst from edges))
where triangles > 0
order by triangles desc;

Column	Type	Description
node_id	bigint	Node identifier
triangles	bigint	Number of triangles containing node

---

Assortativity

Degree assortativity measures whether nodes tend to connect to nodes with similar degree. Positive values mean high-degree nodes connect to other high-degree nodes (assortative). Negative values mean high-degree nodes connect to low-degree nodes (disassortative).

select round(assortativity, 4) as assortativity
from onager_mtr_assortativity((select src, dst from edges));

Column	Type	Description
assortativity	double	Degree correlation coefficient

---

Average Path Length

The average shortest path distance between all pairs of reachable nodes. Lower values indicate a more tightly connected network where information can spread quickly.

select round(avg_path_length, 4) as avg_path
from onager_mtr_avg_path_length((select src, dst from edges));

Column	Type	Description
avg_path_length	double	Mean shortest path across all pairs

---

Graph Density

The fraction of possible edges that actually exist. A density of 1.0 means a complete graph (all possible edges exist), while 0.0 means no edges.

select round(density, 4) as density
from onager_mtr_density((select src, dst from edges));

-- For directed graphs
select round(density, 4) as density
from onager_mtr_density((select src, dst from edges), directed := true);

Column	Type	Description
density	double	Graph density (0.0 to 1.0)

Optional parameters:

• directed (default false): Treat graph as directed

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Complete Example: Network Health Report

Generate a comprehensive report of network properties:

create table network as
select *
from (values (1::bigint, 2::bigint),
             (1, 3),
             (2, 3),
             (2, 4),
             (3, 4),
             (3, 5),
             (4, 5),
             (4, 6),
             (5, 6),
             (6, 7),
             (7, 8),
             (8, 9)) t(src, dst);

-- All metrics in one query
select (select diameter from onager_mtr_diameter((select src, dst from network)))                       as diameter,
       (select radius from onager_mtr_radius((select src, dst from network)))                           as radius,
       (select round(avg_clustering, 3) from onager_mtr_avg_clustering((select src, dst from network))) as clustering,
       (select round(transitivity, 3) from onager_mtr_transitivity((select src, dst from network)))     as transitivity,
       (select round(assortativity, 3)
        from onager_mtr_assortativity((select src, dst from network)))                                  as assortativity;

-- Find the most clustered nodes
select node_id, triangles
from onager_mtr_triangles((select src, dst from network))
order by triangles desc limit 5;