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wuhan
Peter Daszak
武汉三镇之景Black and white photographs of WuhanWuhan ZooSigns in WuhanUnidentified locations in WuhanCounty-level divisions of WuhanWuhan in Chinese charactersWuhan Zall FC

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Wuhan Botanical Garden

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Related Images

Gardens and parks in Wuhan
Visitor attractions in Wuhan

Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Huazhong University of Science and Technology4.0 km away
  • 📍
    Huazhong University of Science and Technology Station4.1 km away
  • Thumbnail for Wuchang Uprising Memorial
    Wuchang Uprising Memorial10.3 km away
  • 📍
    Wuchang Uprising10.4 km away
  • 📍
    wuhan12.9 km away

Network Profile

Overall Strength
i
0.52% of network
(7.7K)
Strength Breakdown
  • This Post (0.52%)
  • Black and white photographs of Wuhan (0.52%)
  • County-level divisions of Wuhan (0.52%)
  • Signs in Wuhan (0.52%)
  • Unidentified locations in Wuhan (0.52%)
  • Wuhan in Chinese characters (0.52%)
  • Wuhan Zall FC (0.52%)
  • Wuhan Zoo (0.52%)
  • 武汉三镇之景 (0.52%)
Dominant nodes (excluded from chart)
wuhan 62.18%Peter Daszak 33.16%
Influence Score
i
8.93% of network
(1.2)
Influence Breakdown
  • This Post (8.93%)
  • wuhan (10.71%)
  • Peter Daszak (8.93%)
  • Black and white photographs of Wuhan (8.93%)
  • County-level divisions of Wuhan (8.93%)
  • Signs in Wuhan (8.93%)
  • Unidentified locations in Wuhan (8.93%)
  • Wuhan in Chinese characters (8.93%)
  • Wuhan Zall FC (8.93%)
  • Wuhan Zoo (8.93%)
  • 武汉三镇之景 (8.93%)
Direct Connections 2

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

// 2. Node Base Values (Local connection strength)
Base_Strength (PV) = $inbound * $outbound = 1 * 1 = 1
Base_Influence (IV) = $inbound / $outbound = 1 / 1 = 1

// 3. Exponential Network Values (accumulating 10 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 120 [wuhan] *
                            64 [Peter Daszak] *
                            1 [Black and white photographs of Wuhan] *
                            1 [County-level divisions of Wuhan] *
                            1 [Signs in Wuhan] *
                            1 [Unidentified locations in Wuhan] *
                            1 [Wuhan in Chinese characters] *
                            1 [Wuhan Zall FC] *
                            1 [Wuhan Zoo] *
                            1 [武汉三镇之景]
                           )

                         = 7.7K

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1.2 [wuhan] *
                            1 [Peter Daszak] *
                            1 [Black and white photographs of Wuhan] *
                            1 [County-level divisions of Wuhan] *
                            1 [Signs in Wuhan] *
                            1 [Unidentified locations in Wuhan] *
                            1 [Wuhan in Chinese characters] *
                            1 [Wuhan Zall FC] *
                            1 [Wuhan Zoo] *
                            1 [武汉三镇之景]
                           )

                         = 1.2
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 10 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.52% (7.7K overall)
  • This Post (0.52%)
  • Black and white photographs of Wuhan (0.52%)
  • County-level divisions of Wuhan (0.52%)
  • Signs in Wuhan (0.52%)
  • Unidentified locations in Wuhan (0.52%)
  • Wuhan in Chinese characters (0.52%)
  • Wuhan Zall FC (0.52%)
  • Wuhan Zoo (0.52%)
  • 武汉三镇之景 (0.52%)
Dominant nodes (excluded from chart)
wuhan 62.18%Peter Daszak 33.16%
Influence Share (vs Direct Neighbours)
8.93% (1.2 overall)
  • This Post (8.93%)
  • wuhan (10.71%)
  • Peter Daszak (8.93%)
  • Black and white photographs of Wuhan (8.93%)
  • County-level divisions of Wuhan (8.93%)
  • Signs in Wuhan (8.93%)
  • Unidentified locations in Wuhan (8.93%)
  • Wuhan in Chinese characters (8.93%)
  • Wuhan Zall FC (8.93%)
  • Wuhan Zoo (8.93%)
  • 武汉三镇之景 (8.93%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
wuhan ↗
Str: 120Inf: 1.2
Peter Daszak ↗
Str: 64Inf: 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 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
Weakest Connections (Lowest Multipliers)
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
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
Peter Daszak ↗
Str: 64Inf: 1
wuhan ↗
Str: 120Inf: 1.2

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

Outbound Tags (1)
wuhan
Inbound Posts (1)
wuhan
Last calculated: Oct 3, 4:30 PM
Wuchang District, Hubei, China25

Related Content

Figures

  • Peter Daszak

Locations

  • wuhan

Topics

  • Wuhan Zoo
  • 武汉三镇之景
  • Black and white photographs of Wuhan
  • County-level divisions of Wuhan
  • Signs in Wuhan
  • Unidentified locations in Wuhan
  • Wuhan in Chinese characters
  • Wuhan Zall FC

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