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Frank Townend BartonThe dog in healthaccidentCannibal AccidentInjury Resrve- AcademyThe dog in health, accident, and disease

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Analyzing Network Connections...

Network Profile

Overall Strength
i
4.60% of network
(5.53M)
Strength Breakdown
  • This Post (4.60%)
  • accident (34.48%)
  • Frank Townend Barton (22.99%)
  • The dog in health, accident, and disease (18.39%)
  • Injury Resrve- Academy (10.34%)
  • Cannibal Accident (4.60%)
  • The dog in health (4.60%)
Influence Score
i
14.29% of network
(0.96)
Influence Breakdown
  • This Post (14.29%)
  • accident (17.14%)
  • Injury Resrve- Academy (14.29%)
  • Cannibal Accident (14.29%)
  • The dog in health (14.29%)
  • The dog in health, accident, and disease (14.29%)
  • Frank Townend Barton (11.43%)
Direct Connections 4

Node & Network Details

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 6 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 30 [accident] *
                            9 [Injury Resrve- Academy] *
                            4 [Cannibal Accident] *
                            4 [The dog in health] *
                            16 [The dog in health, accident, and disease] *
                            20 [Frank Townend Barton]
                           )

                         = 5.53M

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1.2 [accident] *
                            1 [Injury Resrve- Academy] *
                            1 [Cannibal Accident] *
                            1 [The dog in health] *
                            1 [The dog in health, accident, and disease] *
                            0.8 [Frank Townend Barton]
                           )

                         = 0.96
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 6 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
4.60% (5.53M overall)
  • This Post (4.60%)
  • accident (34.48%)
  • Frank Townend Barton (22.99%)
  • The dog in health, accident, and disease (18.39%)
  • Injury Resrve- Academy (10.34%)
  • Cannibal Accident (4.60%)
  • The dog in health (4.60%)
Influence Share (vs Direct Neighbours)
14.29% (0.96 overall)
  • This Post (14.29%)
  • accident (17.14%)
  • Injury Resrve- Academy (14.29%)
  • Cannibal Accident (14.29%)
  • The dog in health (14.29%)
  • The dog in health, accident, and disease (14.29%)
  • Frank Townend Barton (11.43%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
accident ↗
Str: 30Inf: 1.2
Injury Resrve- Academy ↗
Str: 9Inf: 1
Cannibal Accident ↗
Str: 4Inf: 1
The dog in health ↗
Str: 4Inf: 1
The dog in health, accident, and disease ↗
Str: 16Inf: 1
Frank Townend Barton ↗
Str: 20Inf: 0.8
Weakest Connections (Lowest Multipliers)
Frank Townend Barton ↗
Str: 20Inf: 0.8
The dog in health, accident, and disease ↗
Str: 16Inf: 1
The dog in health ↗
Str: 4Inf: 1
Cannibal Accident ↗
Str: 4Inf: 1
Injury Resrve- Academy ↗
Str: 9Inf: 1
accident ↗
Str: 30Inf: 1.2

Connection Health Audit (Red = broken 1-way link)

Outbound Tags (2)
accident
The dog in health, accident, and disease
Inbound Posts (2)
accident
The dog in health, accident, and disease
Last calculated: Jun 18, 3:28 AM
11

Related Content

Figures

  • Frank Townend Barton

Topics

  • Injury Resrve- Academy
  • Cannibal Accident
  • accident
  • The dog in health
  • The dog in health, accident, and disease