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, 3) = 3
$outbound = max(1, 1) = 1
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 3 * 1 = 3
Base_Influence (IV) = $inbound / $outbound = 3 / 1 = 3
// 3. Exponential Network Values (accumulating 20 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 3 *
( 6 [ASIA] *
120 [Taiwan] *
289 [China] *
1 [Architectural elements in Sichuan] *
1 [Construction in Sichuan] *
1 [Sichuanese language] *
4 [Central China] *
1 [East China] *
1 [North China] *
1 [Northeast China] *
1 [South China] *
4 [Fujian Province] *
4 [KaohsiungCity] *
4 [New Taipei City] *
4 [TaichungCity] *
4 [TainanCity] *
4 [TaipeiCity] *
4 [Taiwan Province] *
4 [Taoyuan City] *
25 [Beijing]
)
= 4.09T
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 3 *
( 1.5 [ASIA] *
1.2 [Taiwan] *
1 [China] *
1 [Architectural elements in Sichuan] *
1 [Construction in Sichuan] *
1 [Sichuanese language] *
1 [Central China] *
1 [East China] *
1 [North China] *
1 [Northeast China] *
1 [South China] *
1 [Fujian Province] *
1 [KaohsiungCity] *
1 [New Taipei City] *
1 [TaichungCity] *
1 [TainanCity] *
1 [TaipeiCity] *
1 [Taiwan Province] *
1 [Taoyuan City] *
1 [Beijing]
)
= 5.4
Outbound
3
Tags on post
Inbound
1
Posts tagging this
Connections
20
Total nodes
Base Node Strength
3
Base Node Influence
3
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)China 59.47%Taiwan 24.69%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)
Architecture of Sichuan
BROKEN LINK
