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, 2) = 2
$outbound = max(1, 2) = 2
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 2 * 2 = 4
Base_Influence (IV) = $inbound / $outbound = 2 / 2 = 1
// 3. Exponential Network Values (accumulating 9 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 4 *
( 49 [Jean Baptiste Lully] *
4 [Michel Richard de] *
100 [Jean Philippe Rameau] *
16 [François Couperin] *
4 [François Roberday] *
4 [Nicolas Métru] *
1 [Lycée Jean-Baptiste-Lulli] *
4 [François-Joseph Fétis 1784] *
81 [Charles Marie Widor]
)
= 6.5B
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1 [Jean Baptiste Lully] *
1 [Michel Richard de] *
1 [Jean Philippe Rameau] *
1 [François Couperin] *
1 [François Roberday] *
1 [Nicolas Métru] *
1 [Lycée Jean-Baptiste-Lulli] *
1 [François-Joseph Fétis 1784] *
1 [Charles Marie Widor]
)
= 1
Outbound
2
Tags on post
Inbound
2
Posts tagging this
Connections
9
Total nodes
Base Node Strength
4
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)Jean Philippe Rameau 37.45%Charles Marie Widor 30.34%Jean Baptiste Lully 18.35%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)