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EcoHealth AllianceNIH
Wuhan Institute of Virology
wuhanCoronavirus, Explained
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CNN NewsroomRalph Steven BaricThe University of North Carolina

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Analyzing Network Connections...

Network Profile

Overall Strength
i
3.20% of network
(128.99B)
Strength Breakdown
  • This Post (3.20%)
  • Peter Daszak (22.78%)
  • NIH (10.68%)
  • EcoHealth Alliance (8.54%)
  • CNN Newsroom (4.27%)
  • Ralph Steven Baric (3.20%)
  • Coronavirus, Explained (2.14%)
  • Wuhan Institute of Virology (1.42%)
  • Marion Koopmans (0.36%)
  • Department of Microbiology and Immunology (0.36%)
  • The University of North Carolina (0.36%)
Dominant nodes (excluded from chart)
wuhan 42.70%
Influence Score
i
7.60% of network
(2.43)
Influence Breakdown
  • This Post (7.60%)
  • EcoHealth Alliance (11.41%)
  • Coronavirus, Explained (11.41%)
  • NIH (9.13%)
  • wuhan (9.13%)
  • Peter Daszak (7.60%)
  • Marion Koopmans (7.60%)
  • Wuhan Institute of Virology (7.60%)
  • Ralph Steven Baric (7.60%)
  • Department of Microbiology and Immunology (7.60%)
  • The University of North Carolina (7.60%)
  • CNN Newsroom (5.70%)
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 11 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 9 *
                           ( 24 [EcoHealth Alliance] *
                            6 [Coronavirus, Explained] *
                            30 [NIH] *
                            120 [wuhan] *
                            64 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            4 [Wuhan Institute of Virology] *
                            9 [Ralph Steven Baric] *
                            1 [Department of Microbiology and Immunology] *
                            1 [The University of North Carolina] *
                            12 [CNN Newsroom]
                           )

                         = 128.99B

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1.5 [EcoHealth Alliance] *
                            1.5 [Coronavirus, Explained] *
                            1.2 [NIH] *
                            1.2 [wuhan] *
                            1 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            1 [Wuhan Institute of Virology] *
                            1 [Ralph Steven Baric] *
                            1 [Department of Microbiology and Immunology] *
                            1 [The University of North Carolina] *
                            0.75 [CNN Newsroom]
                           )

                         = 2.43
Outbound 3 Tags on post
Inbound 3 Posts tagging this
Connections 11 Total nodes
Base Node Strength 9
Base Node Influence 1
Strength Share (vs Direct Neighbours)
3.20% (128.99B overall)
  • This Post (3.20%)
  • Peter Daszak (22.78%)
  • NIH (10.68%)
  • EcoHealth Alliance (8.54%)
  • CNN Newsroom (4.27%)
  • Ralph Steven Baric (3.20%)
  • Coronavirus, Explained (2.14%)
  • Wuhan Institute of Virology (1.42%)
  • Marion Koopmans (0.36%)
  • Department of Microbiology and Immunology (0.36%)
  • The University of North Carolina (0.36%)
Dominant nodes (excluded from chart)
wuhan 42.70%
Influence Share (vs Direct Neighbours)
7.60% (2.43 overall)
  • This Post (7.60%)
  • EcoHealth Alliance (11.41%)
  • Coronavirus, Explained (11.41%)
  • NIH (9.13%)
  • wuhan (9.13%)
  • Peter Daszak (7.60%)
  • Marion Koopmans (7.60%)
  • Wuhan Institute of Virology (7.60%)
  • Ralph Steven Baric (7.60%)
  • Department of Microbiology and Immunology (7.60%)
  • The University of North Carolina (7.60%)
  • CNN Newsroom (5.70%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
EcoHealth Alliance ↗
Str: 24Inf: 1.5
Coronavirus, Explained ↗
Str: 6Inf: 1.5
NIH ↗
Str: 30Inf: 1.2
wuhan ↗
Str: 120Inf: 1.2
Peter Daszak ↗
Str: 64Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Wuhan Institute of Virology ↗
Str: 4Inf: 1
Ralph Steven Baric ↗
Str: 9Inf: 1
Department of Microbiology and Immunology ↗
Str: 1Inf: 1
The University of North Carolina ↗
Str: 1Inf: 1
CNN Newsroom ↗
Str: 12Inf: 0.75
Weakest Connections (Lowest Multipliers)
CNN Newsroom ↗
Str: 12Inf: 0.75
The University of North Carolina ↗
Str: 1Inf: 1
Department of Microbiology and Immunology ↗
Str: 1Inf: 1
Ralph Steven Baric ↗
Str: 9Inf: 1
Wuhan Institute of Virology ↗
Str: 4Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Peter Daszak ↗
Str: 64Inf: 1
wuhan ↗
Str: 120Inf: 1.2
NIH ↗
Str: 30Inf: 1.2
Coronavirus, Explained ↗
Str: 6Inf: 1.5
EcoHealth Alliance ↗
Str: 24Inf: 1.5

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

Outbound Tags (3)
Peter Daszak
Ralph Steven Baric
Wuhan Institute of Virology
Inbound Posts (3)
Peter Daszak
Wuhan Institute of Virology
Ralph Steven Baric
Last calculated: Oct 2, 6:54 PM
32

Related Content

Figures

  • Peter Daszak
  • Marion Koopmans

Locations

  • wuhan
  • Wuhan Institute of Virology

Organisations

  • EcoHealth Alliance
  • NIH

Topics

  • CNN Newsroom
  • Coronavirus, Explained
  • Ralph Steven Baric
  • Department of Microbiology and Immunology
  • The University of North Carolina

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