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 10 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 4 *
( 130 [TU Wien] *
4 [EuroCarto 2022] *
9 [Freihaus, TU Wien] *
4 [Gasthof Stadt Ödenburg] *
1 [Geniedirektion] *
4 [Grave of Friedrich Schächter] *
9 [Hauptgebäude der TU Wien] *
4 [Lange Nacht der Forschung TU Wien] *
49 [Technische Universität Wien] *
1 [TU Wien Campus Getreidemarkt]
)
= 528.35M
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1.3 [TU Wien] *
1 [EuroCarto 2022] *
1 [Freihaus, TU Wien] *
1 [Gasthof Stadt Ödenburg] *
1 [Geniedirektion] *
1 [Grave of Friedrich Schächter] *
1 [Hauptgebäude der TU Wien] *
1 [Lange Nacht der Forschung TU Wien] *
1 [Technische Universität Wien] *
1 [TU Wien Campus Getreidemarkt]
)
= 1.3
Outbound
2
Tags on post
Inbound
2
Posts tagging this
Connections
10
Total nodes
Base Node Strength
4
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)TU Wien 59.36%Technische Universität Wien 22.37%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
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Connection Health Audit (Red = broken 1-way link)