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 10 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
= 6 *
( 8 [Johns Hopkins School of Medicine] *
153 [Johns Hopkins] *
170 [Johns Hopkins University] *
72 [Peabody Institute] *
4 [Carey Business School] *
4 [Cosmology Large Angular Scale Surveyor] *
4 [Duncan S. Johnson] *
25 [Johns Hopkins Hospital] *
4 [Joseph Silk] *
4 [White's Hall]
)
= 2.3T
Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
= 1.5 *
( 2 [Johns Hopkins School of Medicine] *
1.89 [Johns Hopkins] *
1.7 [Johns Hopkins University] *
1.13 [Peabody Institute] *
1 [Carey Business School] *
1 [Cosmology Large Angular Scale Surveyor] *
1 [Duncan S. Johnson] *
1 [Johns Hopkins Hospital] *
1 [Joseph Silk] *
1 [White's Hall]
)
= 10.84
Outbound
3
Tags on post
Inbound
2
Posts tagging this
Connections
10
Total nodes
Base Node Strength
6
Base Node Influence
1.5
Strength Share (vs Direct Neighbours)
Dominant nodes (excluded from chart)Johns Hopkins University 37.44%Johns Hopkins 33.70%Peabody Institute 15.86%
Influence Share (vs Direct Neighbours)
Connected Network Hierarchy
Sort list by:
Connection Health Audit (Red = broken 1-way link)
John W. Garrett
BROKEN LINK