Degree centrality — interactive tool

Degree counts the ties an actor holds, and normalised degree divides that by the 5 other actors. It is entirely local, so it sees the neighbourhood and nothing past it. Toggle a tie and only the two actors it touches change degree, while the path-based columns can move for everyone.

Every actor, every measure, recomputed from the ties on screen. Σd is the summed shortest-path distance to the other five actors, in steps. Closeness is 5 divided by that sum. Harmonic is the normalised harmonic closeness, a different measure that averages 1 divided by each distance and treats an unreachable actor as a contribution of zero, with n held at 6 for the whole team rather than shrunk to a component. Betweenness counts each unordered pair once and is not normalised. Clustering is 2e divided by k(k−1) and needs at least two neighbours.
Actor	Degree	k / 5	Strength	Σd (steps)	Closeness	Harmonic	Betweenness	Clustering
Ana (selected)	3	0.60	24	8	0.625	0.767	6	0.33
Ben	2	0.40	21	11	0.455	0.617	0	1.00
Cara	2	0.40	19	11	0.455	0.617	0	1.00
Dan	2	0.40	5	8	0.625	0.700	6	0.00
Eve	2	0.40	11	10	0.500	0.633	4	0.00
Finn	1	0.20	8	14	0.357	0.467	0	—

The team is one component, so every distance is finite and standard closeness has a value for everyone. Clustering is an em dash for Finn, because a coefficient needs at least two neighbours to have any pair to check.

Ties 6 of 15 Components 1 Density 0.40 Standard closeness defined

Ana holds 3 ties, which is 0.60 once divided by the 5 other actors. Degree stops at the first step, so it cannot tell you where those ties lead. The two measures disagree here. Ana leads on degree with 3, while Ana and Dan broker the most pairs at 6. That gap is the reason a network study reports more than one centrality. Strength happens to put the same name on top here, Ana on 24 contacts, because at this setting the heaviest ties and the most ties sit with the same person.

Every figure here is recomputed from the ties currently switched on. Distances come from breadth-first search on steps, or from Dijkstra on 1 divided by the weight when weighted distance is selected, and betweenness comes from the Brandes accumulation over those same shortest paths. Betweenness counts each unordered pair once and is left unnormalised. Bridges and articulation points are found by removing the tie or the actor and recounting the components, not by pattern matching.