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, 3) = 3
// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 3 * 3 = 9
Base_Influence (IV) = $inbound / $outbound = 3 / 3 = 1
// 3. Exponential Network Values (accumulating 13 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 9 *
( 72 [University of Pennsylvania] *
36 [Crypt Sermon] *
4 [Matt Knox] *
1 [Steve Jansson] *
1 [Brooks Wilson] *
1 [Enrique Sagarnaga] *
1 [James Lipczynski] *
25 [Vektor] *
1 [Blake Anderson] *
1 [David DiSanto] *
1 [Erik Nelson] *
1 [Erik Nelson (4)] *
4 [Kathy Change]
)
= 9.33M
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1 *
( 1.13 [University of Pennsylvania] *
1 [Crypt Sermon] *
1 [Matt Knox] *
1 [Steve Jansson] *
1 [Brooks Wilson] *
1 [Enrique Sagarnaga] *
1 [James Lipczynski] *
1 [Vektor] *
1 [Blake Anderson] *
1 [David DiSanto] *
1 [Erik Nelson] *
1 [Erik Nelson (4)] *
1 [Kathy Change]
)
= 1.13
Outbound
3
Tags on post
Inbound
3
Posts tagging this
Connections
13
Total nodes
Base Node Strength
9
Base Node Influence
1
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)University of Pennsylvania 45.57%Crypt Sermon 22.78%Vektor 15.82%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)