Muso

Directory About
Continue with Google
Continue with with Facebook

Top Related Posts

EcoHealth AllianceNIH
Wuhan Institute of Virology
Coronavirus, Explained
Peter Daszak
Marion Koopmans
Shi ZhengliWuhan Botanical GardenCNN NewsroomBlack and white photographs of Wuhan

Recent Logins

View Members Directory

Özkan Konu
Last login: 6 days ago
Comments: 0

Tadhg Kelleher
Last login: 1 week ago
Comments: 0

Anne Marie Bevan
Last login: 1 week ago
Comments: 0

Kevin Greene
Last login: 2 weeks ago
Comments: 0

Seán Millar
Last login: 2 weeks ago
Comments: 0

Kieran Cooke
Last login: 3 weeks ago
Comments: 0

wuhan

wuhan is a location featured in the Muso connections database. Connected to: Black and white photographs of Wuhan, County-level divisions of Wuhan, Gardens and parks in Wuhan, Peter Daszak, Signs in Wuhan, Unidentified locations in Wuhan, Visitor attractions in Wuhan, Wuhan Botanical Garden. Last updated September 2023.

Join the conversation

💬 Know something?

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

Continue with Google Continue with Facebook
Loading Graph...
Press Spacebar to toggle layout

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

Gardens and parks in Wuhan
Visitor attractions in Wuhan
Bangor University
World Health Organization

Analyzing Network Connections...

Loading Map...

Nearest Locations

  • Thumbnail for Wuchang Uprising Memorial
    Wuchang Uprising Memorial5.4 km away
  • 📍
    Wuchang Uprising5.6 km away
  • 📍
    Wuhan Botanical Garden12.9 km away
  • 📍
    Huazhong University of Science and Technology Station14.5 km away
  • 📍
    Huazhong University of Science and Technology14.5 km away

Network Profile

Overall Strength
i
45.45% of network
(7.17B)
Strength Breakdown
  • This Post (45.45%)
  • Shi Zhengli (3.41%)
  • Coronavirus, Explained (2.27%)
  • Wuhan Institute of Virology (1.52%)
  • Marion Koopmans (0.38%)
  • Black and white photographs of Wuhan (0.38%)
  • County-level divisions of Wuhan (0.38%)
  • Signs in Wuhan (0.38%)
  • Unidentified locations in Wuhan (0.38%)
  • Wuhan Botanical Garden (0.38%)
  • Wuhan in Chinese characters (0.38%)
  • Wuhan Zall FC (0.38%)
  • Wuhan Zoo (0.38%)
  • 武汉三镇之景 (0.38%)
Dominant nodes (excluded from chart)
Peter Daszak 24.24%NIH 9.09%EcoHealth Alliance 5.68%CNN Newsroom 4.55%
Influence Score
i
6.12% of network
(3.38)
Influence Breakdown
  • This Post (6.12%)
  • EcoHealth Alliance (8.50%)
  • NIH (7.65%)
  • Coronavirus, Explained (7.65%)
  • Peter Daszak (5.10%)
  • Marion Koopmans (5.10%)
  • Shi Zhengli (5.10%)
  • Wuhan Institute of Virology (5.10%)
  • Black and white photographs of Wuhan (5.10%)
  • County-level divisions of Wuhan (5.10%)
  • Signs in Wuhan (5.10%)
  • Unidentified locations in Wuhan (5.10%)
  • Wuhan Botanical Garden (5.10%)
  • Wuhan in Chinese characters (5.10%)
  • Wuhan Zall FC (5.10%)
  • Wuhan Zoo (5.10%)
  • 武汉三镇之景 (5.10%)
  • CNN Newsroom (3.82%)
Direct Connections 22

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, 12) = 12
$outbound = max(1, 10) = 10

// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 12 * 10 = 120
Base_Influence (IV) = $inbound / $outbound = 12 / 10 = 1.2

// 3. Exponential Network Values (accumulating 17 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 120 *
                           ( 15 [EcoHealth Alliance] *
                            24 [NIH] *
                            6 [Coronavirus, Explained] *
                            64 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            9 [Shi Zhengli] *
                            4 [Wuhan Institute of Virology] *
                            1 [Black and white photographs of Wuhan] *
                            1 [County-level divisions of Wuhan] *
                            1 [Signs in Wuhan] *
                            1 [Unidentified locations in Wuhan] *
                            1 [Wuhan Botanical Garden] *
                            1 [Wuhan in Chinese characters] *
                            1 [Wuhan Zall FC] *
                            1 [Wuhan Zoo] *
                            1 [武汉三镇之景] *
                            12 [CNN Newsroom]
                           )

                         = 7.17B

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1.2 *
                           ( 1.67 [EcoHealth Alliance] *
                            1.5 [NIH] *
                            1.5 [Coronavirus, Explained] *
                            1 [Peter Daszak] *
                            1 [Marion Koopmans] *
                            1 [Shi Zhengli] *
                            1 [Wuhan Institute of Virology] *
                            1 [Black and white photographs of Wuhan] *
                            1 [County-level divisions of Wuhan] *
                            1 [Signs in Wuhan] *
                            1 [Unidentified locations in Wuhan] *
                            1 [Wuhan Botanical Garden] *
                            1 [Wuhan in Chinese characters] *
                            1 [Wuhan Zall FC] *
                            1 [Wuhan Zoo] *
                            1 [武汉三镇之景] *
                            0.75 [CNN Newsroom]
                           )

                         = 3.38
Outbound 12 Tags on post
Inbound 10 Posts tagging this
Connections 17 Total nodes
Base Node Strength 120
Base Node Influence 1.2
Strength Share (vs Direct Neighbours)
45.45% (7.17B overall)
  • This Post (45.45%)
  • Shi Zhengli (3.41%)
  • Coronavirus, Explained (2.27%)
  • Wuhan Institute of Virology (1.52%)
  • Marion Koopmans (0.38%)
  • Black and white photographs of Wuhan (0.38%)
  • County-level divisions of Wuhan (0.38%)
  • Signs in Wuhan (0.38%)
  • Unidentified locations in Wuhan (0.38%)
  • Wuhan Botanical Garden (0.38%)
  • Wuhan in Chinese characters (0.38%)
  • Wuhan Zall FC (0.38%)
  • Wuhan Zoo (0.38%)
  • 武汉三镇之景 (0.38%)
Dominant nodes (excluded from chart)
Peter Daszak 24.24%NIH 9.09%EcoHealth Alliance 5.68%CNN Newsroom 4.55%
Influence Share (vs Direct Neighbours)
6.12% (3.38 overall)
  • This Post (6.12%)
  • EcoHealth Alliance (8.50%)
  • NIH (7.65%)
  • Coronavirus, Explained (7.65%)
  • Peter Daszak (5.10%)
  • Marion Koopmans (5.10%)
  • Shi Zhengli (5.10%)
  • Wuhan Institute of Virology (5.10%)
  • Black and white photographs of Wuhan (5.10%)
  • County-level divisions of Wuhan (5.10%)
  • Signs in Wuhan (5.10%)
  • Unidentified locations in Wuhan (5.10%)
  • Wuhan Botanical Garden (5.10%)
  • Wuhan in Chinese characters (5.10%)
  • Wuhan Zall FC (5.10%)
  • Wuhan Zoo (5.10%)
  • 武汉三镇之景 (5.10%)
  • CNN Newsroom (3.82%)

Connected Network Hierarchy

Top Network Boosters (Highest Multipliers)
EcoHealth Alliance ↗
Str: 15Inf: 1.67
NIH ↗
Str: 24Inf: 1.5
Coronavirus, Explained ↗
Str: 6Inf: 1.5
Peter Daszak ↗
Str: 64Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Shi Zhengli ↗
Str: 9Inf: 1
Wuhan Institute of Virology ↗
Str: 4Inf: 1
Black and white photographs of Wuhan ↗
Str: 1Inf: 1
County-level divisions of Wuhan ↗
Str: 1Inf: 1
Signs in Wuhan ↗
Str: 1Inf: 1
Unidentified locations in Wuhan ↗
Str: 1Inf: 1
Wuhan Botanical Garden ↗
Str: 1Inf: 1
Wuhan in Chinese characters ↗
Str: 1Inf: 1
Wuhan Zall FC ↗
Str: 1Inf: 1
Wuhan Zoo ↗
Str: 1Inf: 1
武汉三镇之景 ↗
Str: 1Inf: 1
CNN Newsroom ↗
Str: 12Inf: 0.75
Weakest Connections (Lowest Multipliers)
CNN Newsroom ↗
Str: 12Inf: 0.75
武汉三镇之景 ↗
Str: 1Inf: 1
Wuhan Zoo ↗
Str: 1Inf: 1
Wuhan Zall FC ↗
Str: 1Inf: 1
Wuhan in Chinese characters ↗
Str: 1Inf: 1
Wuhan Botanical Garden ↗
Str: 1Inf: 1
Unidentified locations in Wuhan ↗
Str: 1Inf: 1
Signs in Wuhan ↗
Str: 1Inf: 1
County-level divisions of Wuhan ↗
Str: 1Inf: 1
Black and white photographs of Wuhan ↗
Str: 1Inf: 1
Wuhan Institute of Virology ↗
Str: 4Inf: 1
Shi Zhengli ↗
Str: 9Inf: 1
Marion Koopmans ↗
Str: 1Inf: 1
Peter Daszak ↗
Str: 64Inf: 1
Coronavirus, Explained ↗
Str: 6Inf: 1.5
NIH ↗
Str: 24Inf: 1.5
EcoHealth Alliance ↗
Str: 15Inf: 1.67

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

Outbound Tags (12)
Black and white photographs of Wuhan
County-level divisions of Wuhan
Gardens and parks in Wuhan
BROKEN LINK
Peter Daszak
Signs in Wuhan
Unidentified locations in Wuhan
Visitor attractions in Wuhan
BROKEN LINK
Wuhan Botanical Garden
Wuhan in Chinese characters
Wuhan Zall FC
Wuhan Zoo
武汉三镇之景
Inbound Posts (10)
Peter Daszak
Black and white photographs of Wuhan
County-level divisions of Wuhan
Signs in Wuhan
Unidentified locations in Wuhan
Wuhan Botanical Garden
Wuhan in Chinese characters
Wuhan Zall FC
Wuhan Zoo
武汉三镇之景
Last calculated: May 26, 7:28 AM
Jiang'an District, Hubei, China37

Related Content

Figures

  • Shi Zhengli
  • Peter Daszak
  • Marion Koopmans

Locations

  • Wuhan Institute of Virology
  • NIH

Organisations

  • EcoHealth Alliance

Topics

  • Wuhan Botanical Garden
  • Wuhan in Chinese characters
  • CNN Newsroom
  • Wuhan Zall FC
  • Coronavirus, Explained
  • Wuhan Zoo
  • Black and white photographs of Wuhan
  • 武汉三镇之景
  • County-level divisions of Wuhan
  • Signs in Wuhan
  • Unidentified locations in Wuhan