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Shock TiltHannu RajalaJonny Mellis ReinholmConny LindFredrik Åkesson

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Connected Bands/Artists

  • Shock Tilt

Connected Musicians

  • Conny Lind
  • Fredrik Åkesson
  • Hannu Rajala
  • Henrik Hildén
  • Jonny Mellis Reinholm
  • Per Häggblom

Network Profile

Overall Strength
i
1.10% of network
(1.8K)
Strength Breakdown
  • This Post (1.10%)
  • Conny Lind (1.10%)
  • Hannu Rajala (1.10%)
  • Jonny Mellis Reinholm (1.10%)
  • Henrik Hildén (1.10%)
  • Per Häggblom (1.10%)
Dominant nodes (excluded from chart)
Shock Tilt 53.85%Fredrik Åkesson 39.56%
Influence Score
i
12.50% of network
(1)
Influence Breakdown
  • This Post (12.50%)
  • Shock Tilt (12.50%)
  • Conny Lind (12.50%)
  • Hannu Rajala (12.50%)
  • Jonny Mellis Reinholm (12.50%)
  • Fredrik Åkesson (12.50%)
  • Henrik Hildén (12.50%)
  • Per Häggblom (12.50%)
Direct Connections 2

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, 1) = 1
$outbound = max(1, 1) = 1

// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 1 * 1 = 1
Base_Influence (IV) = $inbound / $outbound = 1 / 1 = 1

// 3. Exponential Network Values (accumulating 7 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 49 [Shock Tilt] *
                            1 [Conny Lind] *
                            1 [Hannu Rajala] *
                            1 [Jonny Mellis Reinholm] *
                            36 [Fredrik Åkesson] *
                            1 [Henrik Hildén] *
                            1 [Per Häggblom]
                           )

                         = 1.8K

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Shock Tilt] *
                            1 [Conny Lind] *
                            1 [Hannu Rajala] *
                            1 [Jonny Mellis Reinholm] *
                            1 [Fredrik Åkesson] *
                            1 [Henrik Hildén] *
                            1 [Per Häggblom]
                           )

                         = 1
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 7 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.10% (1.8K overall)
  • This Post (1.10%)
  • Conny Lind (1.10%)
  • Hannu Rajala (1.10%)
  • Jonny Mellis Reinholm (1.10%)
  • Henrik Hildén (1.10%)
  • Per Häggblom (1.10%)
Dominant nodes (excluded from chart)
Shock Tilt 53.85%Fredrik Åkesson 39.56%
Influence Share (vs Direct Neighbours)
12.50% (1 overall)
  • This Post (12.50%)
  • Shock Tilt (12.50%)
  • Conny Lind (12.50%)
  • Hannu Rajala (12.50%)
  • Jonny Mellis Reinholm (12.50%)
  • Fredrik Åkesson (12.50%)
  • Henrik Hildén (12.50%)
  • Per Häggblom (12.50%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Shock Tilt ↗
Str: 49Inf: 1
Conny Lind ↗
Str: 1Inf: 1
Hannu Rajala ↗
Str: 1Inf: 1
Jonny Mellis Reinholm ↗
Str: 1Inf: 1
Fredrik Åkesson ↗
Str: 36Inf: 1
Henrik Hildén ↗
Str: 1Inf: 1
Per Häggblom ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
Per Häggblom ↗
Str: 1Inf: 1
Henrik Hildén ↗
Str: 1Inf: 1
Fredrik Åkesson ↗
Str: 36Inf: 1
Jonny Mellis Reinholm ↗
Str: 1Inf: 1
Hannu Rajala ↗
Str: 1Inf: 1
Conny Lind ↗
Str: 1Inf: 1
Shock Tilt ↗
Str: 49Inf: 1

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

Outbound Tags (1)
Shock Tilt
Inbound Posts (1)
Shock Tilt
Last calculated: Oct 27, 10:30 AM
1

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