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 11 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 1 *
( 121 [Dragonland] *
4 [Jonas Heidgert] *
81 [Morten Løwe Sørensen] *
1 [Nicklas Magnusson] *
4 [Anders Hammer] *
1 [Elias Holmlid] *
4 [Jesse Lindskog] *
4 [Christer Pedersen] *
9 [Olof Mörck] *
9 [Olof Mörck] *
9 [Morten LÃ ̧we SÃ ̧rensen]
)
= 1.83B
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1 [Dragonland] *
1 [Jonas Heidgert] *
1 [Morten Løwe Sørensen] *
1 [Nicklas Magnusson] *
1 [Anders Hammer] *
1 [Elias Holmlid] *
1 [Jesse Lindskog] *
1 [Christer Pedersen] *
1 [Olof Mörck] *
1 [Olof Mörck] *
1 [Morten LÃ ̧we SÃ ̧rensen]
)
= 1
Outbound
1
Tags on post
Inbound
1
Posts tagging this
Connections
11
Total nodes
Base Node Strength
1
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)Dragonland 48.79%Morten Løwe Sørensen 32.66%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)