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Analyzing Network Connections...

Network Profile

Overall Strength
i
1.65% of network
(58.53B)
Strength Breakdown
  • This Post (1.65%)
  • Rafael Báez (1.65%)
  • Luna Lauren Velez (0.73%)
  • Josh Hopkins (0.73%)
  • Marisa Ryan (0.73%)
  • Patti D'Arbanville (0.73%)
  • Fatima Faloye (0.18%)
  • Frank Pellegrino (0.18%)
  • George Gore II (0.18%)
  • Jim Moody (0.18%)
  • Jonathan LaPaglia (0.18%)
  • Lee Wong (0.18%)
  • Michael DeLorenzo (0.18%)
  • Michael Michele (0.18%)
  • Jon M. McDonnell (0.18%)
  • Thomas Mikal Ford (0.18%)
  • Victor Colicchio (0.18%)
  • José Pérez (0.18%)
  • Cop Land (0.18%)
  • The Good Nurse (0.18%)
Dominant nodes (excluded from chart)
New York Undercover 80.77%James McCaffrey 4.58%Ernest Abuba 2.93%Lena Cardwell 2.93%
Influence Score
i
4.17% of network
(1)
Influence Breakdown
  • This Post (4.17%)
  • Ernest Abuba (4.17%)
  • Rafael Báez (4.17%)
  • James McCaffrey (4.17%)
  • New York Undercover (4.17%)
  • Lena Cardwell (4.17%)
  • Luna Lauren Velez (4.17%)
  • Fatima Faloye (4.17%)
  • Frank Pellegrino (4.17%)
  • George Gore II (4.17%)
  • Jim Moody (4.17%)
  • Jonathan LaPaglia (4.17%)
  • Josh Hopkins (4.17%)
  • Lee Wong (4.17%)
  • Marisa Ryan (4.17%)
  • Michael DeLorenzo (4.17%)
  • Michael Michele (4.17%)
  • Jon M. McDonnell (4.17%)
  • Thomas Mikal Ford (4.17%)
  • Victor Colicchio (4.17%)
  • Patti D'Arbanville (4.17%)
  • José Pérez (4.17%)
  • Cop Land (4.17%)
  • The Good Nurse (4.17%)
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 23 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 9 *
                           ( 16 [Ernest Abuba] *
                            9 [Rafael Báez] *
                            25 [James McCaffrey] *
                            441 [New York Undercover] *
                            16 [Lena Cardwell] *
                            4 [Luna Lauren Velez] *
                            1 [Fatima Faloye] *
                            1 [Frank Pellegrino] *
                            1 [George Gore II] *
                            1 [Jim Moody] *
                            1 [Jonathan LaPaglia] *
                            4 [Josh Hopkins] *
                            1 [Lee Wong] *
                            4 [Marisa Ryan] *
                            1 [Michael DeLorenzo] *
                            1 [Michael Michele] *
                            1 [Jon M. McDonnell] *
                            1 [Thomas Mikal Ford] *
                            1 [Victor Colicchio] *
                            4 [Patti D'Arbanville] *
                            1 [José Pérez] *
                            1 [Cop Land] *
                            1 [The Good Nurse]
                           )

                         = 58.53B

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Ernest Abuba] *
                            1 [Rafael Báez] *
                            1 [James McCaffrey] *
                            1 [New York Undercover] *
                            1 [Lena Cardwell] *
                            1 [Luna Lauren Velez] *
                            1 [Fatima Faloye] *
                            1 [Frank Pellegrino] *
                            1 [George Gore II] *
                            1 [Jim Moody] *
                            1 [Jonathan LaPaglia] *
                            1 [Josh Hopkins] *
                            1 [Lee Wong] *
                            1 [Marisa Ryan] *
                            1 [Michael DeLorenzo] *
                            1 [Michael Michele] *
                            1 [Jon M. McDonnell] *
                            1 [Thomas Mikal Ford] *
                            1 [Victor Colicchio] *
                            1 [Patti D'Arbanville] *
                            1 [José Pérez] *
                            1 [Cop Land] *
                            1 [The Good Nurse]
                           )

                         = 1
Outbound 3 Tags on post
Inbound 3 Posts tagging this
Connections 23 Total nodes
Base Node Strength 9
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.65% (58.53B overall)
  • This Post (1.65%)
  • Rafael Báez (1.65%)
  • Luna Lauren Velez (0.73%)
  • Josh Hopkins (0.73%)
  • Marisa Ryan (0.73%)
  • Patti D'Arbanville (0.73%)
  • Fatima Faloye (0.18%)
  • Frank Pellegrino (0.18%)
  • George Gore II (0.18%)
  • Jim Moody (0.18%)
  • Jonathan LaPaglia (0.18%)
  • Lee Wong (0.18%)
  • Michael DeLorenzo (0.18%)
  • Michael Michele (0.18%)
  • Jon M. McDonnell (0.18%)
  • Thomas Mikal Ford (0.18%)
  • Victor Colicchio (0.18%)
  • José Pérez (0.18%)
  • Cop Land (0.18%)
  • The Good Nurse (0.18%)
Dominant nodes (excluded from chart)
New York Undercover 80.77%James McCaffrey 4.58%Ernest Abuba 2.93%Lena Cardwell 2.93%
Influence Share (vs Direct Neighbours)
4.17% (1 overall)
  • This Post (4.17%)
  • Ernest Abuba (4.17%)
  • Rafael Báez (4.17%)
  • James McCaffrey (4.17%)
  • New York Undercover (4.17%)
  • Lena Cardwell (4.17%)
  • Luna Lauren Velez (4.17%)
  • Fatima Faloye (4.17%)
  • Frank Pellegrino (4.17%)
  • George Gore II (4.17%)
  • Jim Moody (4.17%)
  • Jonathan LaPaglia (4.17%)
  • Josh Hopkins (4.17%)
  • Lee Wong (4.17%)
  • Marisa Ryan (4.17%)
  • Michael DeLorenzo (4.17%)
  • Michael Michele (4.17%)
  • Jon M. McDonnell (4.17%)
  • Thomas Mikal Ford (4.17%)
  • Victor Colicchio (4.17%)
  • Patti D'Arbanville (4.17%)
  • José Pérez (4.17%)
  • Cop Land (4.17%)
  • The Good Nurse (4.17%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Ernest Abuba ↗
Str: 16Inf: 1
Rafael Báez ↗
Str: 9Inf: 1
James McCaffrey ↗
Str: 25Inf: 1
New York Undercover ↗
Str: 441Inf: 1
Lena Cardwell ↗
Str: 16Inf: 1
Luna Lauren Velez ↗
Str: 4Inf: 1
Fatima Faloye ↗
Str: 1Inf: 1
Frank Pellegrino ↗
Str: 1Inf: 1
George Gore II ↗
Str: 1Inf: 1
Jim Moody ↗
Str: 1Inf: 1
Jonathan LaPaglia ↗
Str: 1Inf: 1
Josh Hopkins ↗
Str: 4Inf: 1
Lee Wong ↗
Str: 1Inf: 1
Marisa Ryan ↗
Str: 4Inf: 1
Michael DeLorenzo ↗
Str: 1Inf: 1
Michael Michele ↗
Str: 1Inf: 1
Jon M. McDonnell ↗
Str: 1Inf: 1
Thomas Mikal Ford ↗
Str: 1Inf: 1
Victor Colicchio ↗
Str: 1Inf: 1
Patti D'Arbanville ↗
Str: 4Inf: 1
José Pérez ↗
Str: 1Inf: 1
Cop Land ↗
Str: 1Inf: 1
The Good Nurse ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
The Good Nurse ↗
Str: 1Inf: 1
Cop Land ↗
Str: 1Inf: 1
José Pérez ↗
Str: 1Inf: 1
Patti D'Arbanville ↗
Str: 4Inf: 1
Victor Colicchio ↗
Str: 1Inf: 1
Thomas Mikal Ford ↗
Str: 1Inf: 1
Jon M. McDonnell ↗
Str: 1Inf: 1
Michael Michele ↗
Str: 1Inf: 1
Michael DeLorenzo ↗
Str: 1Inf: 1
Marisa Ryan ↗
Str: 4Inf: 1
Lee Wong ↗
Str: 1Inf: 1
Josh Hopkins ↗
Str: 4Inf: 1
Jonathan LaPaglia ↗
Str: 1Inf: 1
Jim Moody ↗
Str: 1Inf: 1
George Gore II ↗
Str: 1Inf: 1
Frank Pellegrino ↗
Str: 1Inf: 1
Fatima Faloye ↗
Str: 1Inf: 1
Luna Lauren Velez ↗
Str: 4Inf: 1
Lena Cardwell ↗
Str: 16Inf: 1
New York Undercover ↗
Str: 441Inf: 1
James McCaffrey ↗
Str: 25Inf: 1
Rafael Báez ↗
Str: 9Inf: 1
Ernest Abuba ↗
Str: 16Inf: 1

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

Outbound Tags (3)
Cop Land
New York Undercover
The Good Nurse
Inbound Posts (3)
New York Undercover
Cop Land
The Good Nurse
Last calculated: Jun 12, 10:18 PM
28

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Actors

  • James McCaffrey
  • Jonathan LaPaglia
  • Josh Hopkins
  • Patti D'Arbanville
  • Lena Cardwell
  • Lee Wong
  • José Pérez
  • Luna Lauren Velez
  • Marisa Ryan
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  • Michael Michele
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  • George Gore II
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Films

  • Victor Colicchio
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