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Jay BaruchelMayor of KingstownGeorge TchortovMillion Dollar BabyThis Is the EndLife with Judy Garland: Me and My ShadowsAlison PillHow to Train Your DragonDan in Real Life
RoboCop

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Analyzing Network Connections...

Network Profile

Overall Strength
i
0.43% of network
(6.50 × 1021)
Strength Breakdown
  • This Post (0.43%)
  • RoboCop (19.01%)
  • Star Trek: Picard (19.01%)
  • Million Dollar Baby (15.40%)
  • Mayor of Kingstown (15.40%)
  • Life with Judy Garland: Me and My Shadows (15.40%)
  • This Is the End (12.17%)
  • Jay Baruchel (1.19%)
  • Alison Pill (0.76%)
  • George Tchortov (0.43%)
  • Dan in Real Life (0.43%)
  • How to Train Your Dragon (0.19%)
  • How to Train Your Dragon: The Hidden World (0.19%)
Influence Score
i
7.69% of network
(1)
Influence Breakdown
  • This Post (7.69%)
  • Million Dollar Baby (7.69%)
  • Jay Baruchel (7.69%)
  • This Is the End (7.69%)
  • How to Train Your Dragon (7.69%)
  • How to Train Your Dragon: The Hidden World (7.69%)
  • Mayor of Kingstown (7.69%)
  • George Tchortov (7.69%)
  • RoboCop (7.69%)
  • Star Trek: Picard (7.69%)
  • Life with Judy Garland: Me and My Shadows (7.69%)
  • Alison Pill (7.69%)
  • Dan in Real Life (7.69%)
Direct Connections 6

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, 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 12 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 9 *
                           ( 324 [Million Dollar Baby] *
                            25 [Jay Baruchel] *
                            256 [This Is the End] *
                            4 [How to Train Your Dragon] *
                            4 [How to Train Your Dragon: The Hidden World] *
                            324 [Mayor of Kingstown] *
                            9 [George Tchortov] *
                            400 [RoboCop] *
                            400 [Star Trek: Picard] *
                            324 [Life with Judy Garland: Me and My Shadows] *
                            16 [Alison Pill] *
                            9 [Dan in Real Life]
                           )

                         = 6.50 x 10^21

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Million Dollar Baby] *
                            1 [Jay Baruchel] *
                            1 [This Is the End] *
                            1 [How to Train Your Dragon] *
                            1 [How to Train Your Dragon: The Hidden World] *
                            1 [Mayor of Kingstown] *
                            1 [George Tchortov] *
                            1 [RoboCop] *
                            1 [Star Trek: Picard] *
                            1 [Life with Judy Garland: Me and My Shadows] *
                            1 [Alison Pill] *
                            1 [Dan in Real Life]
                           )

                         = 1
Outbound 3 Tags on post
Inbound 3 Posts tagging this
Connections 12 Total nodes
Base Node Strength 9
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.43% (6.50 × 1021 overall)
  • This Post (0.43%)
  • RoboCop (19.01%)
  • Star Trek: Picard (19.01%)
  • Million Dollar Baby (15.40%)
  • Mayor of Kingstown (15.40%)
  • Life with Judy Garland: Me and My Shadows (15.40%)
  • This Is the End (12.17%)
  • Jay Baruchel (1.19%)
  • Alison Pill (0.76%)
  • George Tchortov (0.43%)
  • Dan in Real Life (0.43%)
  • How to Train Your Dragon (0.19%)
  • How to Train Your Dragon: The Hidden World (0.19%)
Influence Share (vs Direct Neighbours)
7.69% (1 overall)
  • This Post (7.69%)
  • Million Dollar Baby (7.69%)
  • Jay Baruchel (7.69%)
  • This Is the End (7.69%)
  • How to Train Your Dragon (7.69%)
  • How to Train Your Dragon: The Hidden World (7.69%)
  • Mayor of Kingstown (7.69%)
  • George Tchortov (7.69%)
  • RoboCop (7.69%)
  • Star Trek: Picard (7.69%)
  • Life with Judy Garland: Me and My Shadows (7.69%)
  • Alison Pill (7.69%)
  • Dan in Real Life (7.69%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Million Dollar Baby ↗
Str: 324Inf: 1
Jay Baruchel ↗
Str: 25Inf: 1
This Is the End ↗
Str: 256Inf: 1
How to Train Your Dragon ↗
Str: 4Inf: 1
How to Train Your Dragon: The Hidden World ↗
Str: 4Inf: 1
Mayor of Kingstown ↗
Str: 324Inf: 1
George Tchortov ↗
Str: 9Inf: 1
RoboCop ↗
Str: 400Inf: 1
Star Trek: Picard ↗
Str: 400Inf: 1
Life with Judy Garland: Me and My Shadows ↗
Str: 324Inf: 1
Alison Pill ↗
Str: 16Inf: 1
Dan in Real Life ↗
Str: 9Inf: 1
Weakest Connections (Lowest Multipliers)
Dan in Real Life ↗
Str: 9Inf: 1
Alison Pill ↗
Str: 16Inf: 1
Life with Judy Garland: Me and My Shadows ↗
Str: 324Inf: 1
Star Trek: Picard ↗
Str: 400Inf: 1
RoboCop ↗
Str: 400Inf: 1
George Tchortov ↗
Str: 9Inf: 1
Mayor of Kingstown ↗
Str: 324Inf: 1
How to Train Your Dragon: The Hidden World ↗
Str: 4Inf: 1
How to Train Your Dragon ↗
Str: 4Inf: 1
This Is the End ↗
Str: 256Inf: 1
Jay Baruchel ↗
Str: 25Inf: 1
Million Dollar Baby ↗
Str: 324Inf: 1

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

Outbound Tags (3)
Alison Pill
George Tchortov
Jay Baruchel
Inbound Posts (3)
George Tchortov
Jay Baruchel
Alison Pill
Last calculated: Jun 16, 2:46 AM
26

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Actors

  • George Tchortov
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  • Alison Pill

Films

  • How to Train Your Dragon: The Hidden World
  • Mayor of Kingstown
  • RoboCop
  • Million Dollar Baby
  • Star Trek: Picard
  • Life with Judy Garland: Me and My Shadows
  • This Is the End
  • How to Train Your Dragon
  • Dan in Real Life