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, 2) = 2
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 3 * 2 = 6
Base_Influence (IV) = $inbound / $outbound = 3 / 2 = 1.5
// 3. Exponential Network Values (accumulating 12 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 6 *
( 2 [Sars-Cov-2] *
24 [EcoHealth Alliance] *
24 [NIH] *
120 [wuhan] *
1 [SARS-CoV-2 animations] *
1 [SARS-CoV-2 lifecycle] *
64 [Peter Daszak] *
1 [Marion Koopmans] *
9 [Shi Zhengli] *
4 [Wuhan Institute of Virology] *
1 [Explained] *
12 [CNN Newsroom]
)
= 22.93B
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1.5 *
( 2 [Sars-Cov-2] *
1.5 [EcoHealth Alliance] *
1.5 [NIH] *
1.2 [wuhan] *
1 [SARS-CoV-2 animations] *
1 [SARS-CoV-2 lifecycle] *
1 [Peter Daszak] *
1 [Marion Koopmans] *
1 [Shi Zhengli] *
1 [Wuhan Institute of Virology] *
1 [Explained] *
0.75 [CNN Newsroom]
)
= 6.08
Outbound
3
Tags on post
Inbound
2
Posts tagging this
Connections
12
Total nodes
Base Node Strength
6
Base Node Influence
1.5
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)wuhan 44.61%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)