Explore clustering coefficient, path length, and small-world structure. Click nodes to inspect.
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Nodes: 0
Edges: 0
Global Clustering C: 0
Avg Path Length L: —
Selected node C: —
Clustering coefficient C measures the fraction of a node's neighbors that are also neighbors of each other. Cᵢ = 2eᵢ / kᵢ(kᵢ−1) where eᵢ = actual edges among neighbors, kᵢ = degree. Global C = average over all nodes.
Watts-Strogatz small-world model: start from a regular ring lattice (high C, high L), rewire edges randomly with probability p. Even small p dramatically reduces L while keeping C high — the "small-world" property. Real social networks, power grids, and the C. elegans connectome all show this.
Barabási-Albert scale-free networks grow by preferential attachment: new nodes connect to existing nodes with probability proportional to their degree, creating hubs and power-law degree distributions P(k) ~ k^−γ.
Click on any node to highlight its neighbors and compute its local clustering coefficient.