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, 1) = 1
$outbound = max(1, 1) = 1
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 1 * 1 = 1
Base_Influence (IV) = $inbound / $outbound = 1 / 1 = 1
// 3. Exponential Network Values (accumulating 13 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 1 *
( 286 [Czech Technical University] *
1 [Aleš Čepek] *
1 [Cyril Purkyně] *
9 [Czech Institute of Informatics, Robotics and Cybernetics] *
1 [Faculty of Czech Technical University] *
16 [Faculty of Nuclear Sciences and Physical Engineering] *
1 [Kloknerův ústav] *
1 [Solar Air Water Earth Resource] *
1 [Univerzitní centrum energeticky efektivních budov ČVUT] *
1 [Víceúčelové sportoviště Kotlářka] *
9 [Czech Technical University, Břehová 7] *
9 [Czech Technical University, Trojanova 13] *
4 [Trojanova 13]
)
= 13.34M
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1.69 [Czech Technical University] *
1 [Aleš Čepek] *
1 [Cyril Purkyně] *
1 [Czech Institute of Informatics, Robotics and Cybernetics] *
1 [Faculty of Czech Technical University] *
1 [Faculty of Nuclear Sciences and Physical Engineering] *
1 [Kloknerův ústav] *
1 [Solar Air Water Earth Resource] *
1 [Univerzitní centrum energeticky efektivních budov ČVUT] *
1 [Víceúčelové sportoviště Kotlářka] *
1 [Czech Technical University, Břehová 7] *
1 [Czech Technical University, Trojanova 13] *
1 [Trojanova 13]
)
= 1.69
Outbound
1
Tags on post
Inbound
1
Posts tagging this
Connections
13
Total nodes
Base Node Strength
1
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)Czech Technical University 83.87%Faculty of Nuclear Sciences and Physical Engineering 4.69%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
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Connection Health Audit (Red = broken 1-way link)