Graph curvature does not have one universal formula because discrete geometry can encode local structure in several ways. One definition emphasizes overlap among neighboring vertices, another uses triangle structure, and others use shortest-path distances or transport between probability distributions on adjacent vertices. The chosen formulation determines which aspect of branching, clustering, or connectivity becomes most visible.
Neighborhood overlap measures how much adjacent vertices share their surrounding connections. Substantial overlap signals that nearby parts of the graph participate in common local structure, while limited overlap indicates a more separated or branching arrangement. This perspective is useful when the goal is to distinguish clustered regions from locations where connectivity changes sharply.
Shortest-path-based measures examine the distances required to move through the graph, whereas transport-based measures compare probability distributions associated with adjacent vertices. The first emphasizes route geometry; the second emphasizes how mass or probability must be rearranged across local neighborhoods. Comparing them can reveal different aspects of the same network rather than treating curvature as a single structural property.
Triangles provide a direct local indication of tightly interconnected neighborhoods. When triangle structure is included, the calculation becomes sensitive to whether neighboring vertices are linked to one another rather than merely attached through a common vertex. That makes triangle-aware curvature useful for separating locally cohesive portions of a graph from strongly branching or weakly clustered portions.
To study a graph, first choose the curvature formulation that matches the structural question, then identify the required local information, such as neighborhood overlap, triangles, shortest paths, or adjacent-vertex probability distributions. Calculate the resulting values for relevant vertices or edges and compare their patterns. This workflow links the mathematical measure to a specific interpretation of network shape.
Curvature measurements can highlight where local connectivity differs from the surrounding graph. Regions with strong local interconnection may stand out from areas where paths narrow or branching changes, helping locate bottlenecks and highly connected zones. Researchers can then use these structural contrasts to assess network organization or examine how the graph changes over time.
In mathematics, graph curvature extends geometric reasoning to discrete structures, while in network science it provides a way to study organization without relying on smooth surfaces. Its structural information can support analyses of data organization, robustness, diffusion, and processes evolving on complex networks. The specific interpretation remains tied to the curvature definition selected.