Muso

Directory About
Continue with Google
Continue with with Facebook

Top Related Posts

EcoHealth AllianceNIHwuhanShi ZhengliCoronavirus, Explained
Marion Koopmans
Peter Daszak
CNN NewsroomRalph Steven Baric

Recent Logins

View Members Directory

Tadhg Kelleher
Last login: 12 hours ago
Comments: 0

jacierlogyn
Last login: 5 days ago
Comments: 0

Eileen Lally
Last login: 4 weeks ago
Comments: 0

Özkan Konu
Last login: 2 months ago
Comments: 0

Anne Marie Bevan
Last login: 2 months ago
Comments: 0

Kevin Greene
Last login: 2 months ago
Comments: 0

Wuhan Institute of Virology

Loading Graph...
Press Spacebar to toggle layout
Join the conversation

💬 Know something?

Sign in to leave a note, add a photo, or make a connection.

Continue with Google Continue with Facebook

Keep Muso free

Muso is built by one person, for the love of it. no investors — just your support.

€10
Covers an hour of research
Most popular
€25
Keeps the archive running
€50
Funds a full week of work
✎ Enter my own amount
€5
per month · cancel any time

You'll confirm the amount on the next screen

Donate €25 →
Secure checkout via Stripe  ·  No account needed

Related Images

Bangor University
World Health Organization

Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Wuchang Uprising18.6 km away
  • Thumbnail for Wuchang Uprising Memorial
    Wuchang Uprising Memorial18.8 km away
  • 📍
    Huazhong University of Science and Technology Station20.4 km away
  • 📍
    Huazhong University of Science and Technology20.5 km away
  • 📍
    wuhan23.6 km away

Network Profile

Overall Strength
i
1.47% of network
(103.2B)
Strength Breakdown
  • This Post (1.47%)
  • Peter Daszak (23.44%)
  • EcoHealth Alliance (8.79%)
  • NIH (8.79%)
  • CNN Newsroom (4.40%)
  • Shi Zhengli (3.30%)
  • Ralph Steven Baric (3.30%)
  • Coronavirus, Explained (2.20%)
  • Marion Koopmans (0.37%)
Dominant nodes (excluded from chart)
wuhan 43.96%
Influence Score
i
8.73% of network
(3.04)
Influence Breakdown
  • This Post (8.73%)
  • EcoHealth Alliance (13.10%)
  • NIH (13.10%)
  • Coronavirus, Explained (13.10%)
  • wuhan (10.48%)
  • Peter Daszak (8.73%)
  • Marion Koopmans (8.73%)
  • Shi Zhengli (8.73%)
  • Ralph Steven Baric (8.73%)
  • CNN Newsroom (6.55%)
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 9 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 24 [EcoHealth Alliance] *
                            24 [NIH] *
                            6 [Coronavirus, Explained] *
                            120 [wuhan] *
                            64 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            9 [Shi Zhengli] *
                            9 [Ralph Steven Baric] *
                            12 [CNN Newsroom]
                           )

                         = 103.2B

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1.5 [EcoHealth Alliance] *
                            1.5 [NIH] *
                            1.5 [Coronavirus, Explained] *
                            1.2 [wuhan] *
                            1 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            1 [Shi Zhengli] *
                            1 [Ralph Steven Baric] *
                            0.75 [CNN Newsroom]
                           )

                         = 3.04
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 9 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.47% (103.2B overall)
  • This Post (1.47%)
  • Peter Daszak (23.44%)
  • EcoHealth Alliance (8.79%)
  • NIH (8.79%)
  • CNN Newsroom (4.40%)
  • Shi Zhengli (3.30%)
  • Ralph Steven Baric (3.30%)
  • Coronavirus, Explained (2.20%)
  • Marion Koopmans (0.37%)
Dominant nodes (excluded from chart)
wuhan 43.96%
Influence Share (vs Direct Neighbours)
8.73% (3.04 overall)
  • This Post (8.73%)
  • EcoHealth Alliance (13.10%)
  • NIH (13.10%)
  • Coronavirus, Explained (13.10%)
  • wuhan (10.48%)
  • Peter Daszak (8.73%)
  • Marion Koopmans (8.73%)
  • Shi Zhengli (8.73%)
  • Ralph Steven Baric (8.73%)
  • CNN Newsroom (6.55%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
EcoHealth Alliance ↗
Str: 24Inf: 1.5
NIH ↗
Str: 24Inf: 1.5
Coronavirus, Explained ↗
Str: 6Inf: 1.5
wuhan ↗
Str: 120Inf: 1.2
Peter Daszak ↗
Str: 64Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Shi Zhengli ↗
Str: 9Inf: 1
Ralph Steven Baric ↗
Str: 9Inf: 1
CNN Newsroom ↗
Str: 12Inf: 0.75
Weakest Connections (Lowest Multipliers)
CNN Newsroom ↗
Str: 12Inf: 0.75
Ralph Steven Baric ↗
Str: 9Inf: 1
Shi Zhengli ↗
Str: 9Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Peter Daszak ↗
Str: 64Inf: 1
wuhan ↗
Str: 120Inf: 1.2
Coronavirus, Explained ↗
Str: 6Inf: 1.5
NIH ↗
Str: 24Inf: 1.5
EcoHealth Alliance ↗
Str: 24Inf: 1.5

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

Outbound Tags (2)
Peter Daszak
Shi Zhengli
Inbound Posts (2)
Peter Daszak
Shi Zhengli
Last calculated: Jul 4, 2:45 AM
Jiangxia District, Hubei, China38

Related Content

Figures

  • Peter Daszak
  • Marion Koopmans
  • Shi Zhengli

Locations

  • NIH
  • wuhan

Organisations

  • EcoHealth Alliance

Topics

  • Ralph Steven Baric
  • CNN Newsroom
  • Coronavirus, Explained