Every node's importance depends on which lens you use. Degree just counts direct links. Betweenness counts the fraction of all shortest paths between other node-pairs that pass through this node — high values mark bridges between clusters. Closeness is the inverse of the average shortest-path distance to everyone else — high values mark nodes that can reach the whole network fast. Eigenvector weighs a node by the importance of its neighbours (recursively) — it is the mechanism behind PageRank.
deg(v) = |neighbors(v)| / (n-1)
btw(v) = Σ σ_st(v)/σ_st over all pairs s,t
clo(v) = (n-1) / Σ dist(v,u)
eig(v) = (1/λ) Σ A(v,u)·eig(u) [power iteration]
- Switch metrics to see the same graph re-ranked — a hub by degree is not always a bridge by betweenness.
- Node size and colour both track the selected metric's value.
- "Remove top-ranked node" deletes whichever node currently scores highest, then recomputes every metric — watch how quickly the network fragments when you strip out its top betweenness bridge versus its top degree hub.
- Drag to orbit, scroll to zoom, click a node for its full metric breakdown.