How is this calculated?
The math continuously tracks how strongly this post is connected to the rest of the network. Every tag forms a 2-way link. The base stats determine personal node strength, and the pie charts below show this node's share against its direct neighbours.
// 1. Base variables (floored at 1 to prevent zero-multiplication math errors)
$inbound = max(1, 6) = 6
$outbound = max(1, 6) = 6
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 6 * 6 = 36
Base_Influence (IV) = $inbound / $outbound = 6 / 6 = 1
// 3. Exponential Network Values (accumulating 10 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 36 *
( 30 [KÄLTER] *
4 [Kalterer See] *
169 [Oberplanitzing] *
1 [Rastenbachklamm] *
25 [St. Josef am See] *
1 [Unterplanitzing 27] *
1 [Unterplanitzing 28] *
1 [Unterplanitzing 29] *
1 [Unterplanitzing 30] *
1 [Unterplanitzing 31]
)
= 18.25M
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1.2 [KÄLTER] *
1 [Kalterer See] *
1 [Oberplanitzing] *
1 [Rastenbachklamm] *
1 [St. Josef am See] *
1 [Unterplanitzing 27] *
1 [Unterplanitzing 28] *
1 [Unterplanitzing 29] *
1 [Unterplanitzing 30] *
1 [Unterplanitzing 31]
)
= 1.2
Outbound
6
Tags on post
Inbound
6
Posts tagging this
Connections
10
Total nodes
Base Node Strength
36
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)Oberplanitzing 62.59%KÄLTER 11.11%St. Josef am See 9.26%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)
