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Analyzing Network Connections...

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Nearest Locations

  • 📍
    Chernobyl139.8 km away
  • 📍
    Memorial Museum of Oleksandr Dovzhenko142.9 km away
  • 📍
    Mahilioŭ Castle169.7 km away
  • 📍
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    Kamsamoĺskaja Street 8A, Mahilioŭ170.0 km away

Network Profile

Overall Strength
i
1.22% of network
(631.7M)
Strength Breakdown
  • This Post (1.22%)
  • Vitebsk (2.74%)
  • Minsk (1.82%)
  • Brest (1.22%)
  • Grodno (1.22%)
  • Mogilev (1.22%)
  • Jēkabpils (1.22%)
  • Daugavpils (0.30%)
  • Jūrmala (0.30%)
  • Liepāja (0.30%)
  • Rēzekne (0.30%)
  • Riga (0.30%)
  • Valmiera (0.30%)
  • Ventspils (0.30%)
  • Ruba (0.30%)
Dominant nodes (excluded from chart)
Latvia 72.34%Belarus 14.59%
Influence Score
i
5.73% of network
(1.37)
Influence Breakdown
  • This Post (5.73%)
  • Minsk (8.59%)
  • Latvia (6.95%)
  • Brest (5.73%)
  • Grodno (5.73%)
  • Mogilev (5.73%)
  • Vitebsk (5.73%)
  • Daugavpils (5.73%)
  • Jēkabpils (5.73%)
  • Jūrmala (5.73%)
  • Liepāja (5.73%)
  • Rēzekne (5.73%)
  • Riga (5.73%)
  • Valmiera (5.73%)
  • Ventspils (5.73%)
  • Ruba (5.73%)
  • Belarus (4.29%)
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 16 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 6 [Minsk] *
                            238 [Latvia] *
                            4 [Brest] *
                            4 [Grodno] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            1 [Daugavpils] *
                            4 [Jēkabpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ventspils] *
                            1 [Ruba] *
                            48 [Belarus]
                           )

                         = 631.7M

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1.5 [Minsk] *
                            1.21 [Latvia] *
                            1 [Brest] *
                            1 [Grodno] *
                            1 [Mogilev] *
                            1 [Vitebsk] *
                            1 [Daugavpils] *
                            1 [Jēkabpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ventspils] *
                            1 [Ruba] *
                            0.75 [Belarus]
                           )

                         = 1.37
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 16 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.22% (631.7M overall)
  • This Post (1.22%)
  • Vitebsk (2.74%)
  • Minsk (1.82%)
  • Brest (1.22%)
  • Grodno (1.22%)
  • Mogilev (1.22%)
  • Jēkabpils (1.22%)
  • Daugavpils (0.30%)
  • Jūrmala (0.30%)
  • Liepāja (0.30%)
  • Rēzekne (0.30%)
  • Riga (0.30%)
  • Valmiera (0.30%)
  • Ventspils (0.30%)
  • Ruba (0.30%)
Dominant nodes (excluded from chart)
Latvia 72.34%Belarus 14.59%
Influence Share (vs Direct Neighbours)
5.73% (1.37 overall)
  • This Post (5.73%)
  • Minsk (8.59%)
  • Latvia (6.95%)
  • Brest (5.73%)
  • Grodno (5.73%)
  • Mogilev (5.73%)
  • Vitebsk (5.73%)
  • Daugavpils (5.73%)
  • Jēkabpils (5.73%)
  • Jūrmala (5.73%)
  • Liepāja (5.73%)
  • Rēzekne (5.73%)
  • Riga (5.73%)
  • Valmiera (5.73%)
  • Ventspils (5.73%)
  • Ruba (5.73%)
  • Belarus (4.29%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Minsk ↗
Str: 6Inf: 1.5
Latvia ↗
Str: 238Inf: 1.21
Brest ↗
Str: 4Inf: 1
Grodno ↗
Str: 4Inf: 1
Mogilev ↗
Str: 4Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Jūrmala ↗
Str: 1Inf: 1
Liepāja ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 1Inf: 1
Riga ↗
Str: 1Inf: 1
Valmiera ↗
Str: 1Inf: 1
Ventspils ↗
Str: 1Inf: 1
Ruba ↗
Str: 1Inf: 1
Belarus ↗
Str: 48Inf: 0.75
Weakest Connections (Lowest Multipliers)
Belarus ↗
Str: 48Inf: 0.75
Ruba ↗
Str: 1Inf: 1
Ventspils ↗
Str: 1Inf: 1
Valmiera ↗
Str: 1Inf: 1
Riga ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 1Inf: 1
Liepāja ↗
Str: 1Inf: 1
Jūrmala ↗
Str: 1Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Mogilev ↗
Str: 4Inf: 1
Grodno ↗
Str: 4Inf: 1
Brest ↗
Str: 4Inf: 1
Latvia ↗
Str: 238Inf: 1.21
Minsk ↗
Str: 6Inf: 1.5

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

Outbound Tags (2)
Belarus
Latvia
Inbound Posts (2)
Latvia
Belarus
Last calculated: Sep 3, 11:41 PM
Homyel, Homyel Region, Belarus40

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