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Brest

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Related Images

Gomel
Minsk
Society of Latvia
Grodno
Riga
Jelgava
Jēkabpils
Objects in Brest, France
Lithuania in Russo-Ukrainian War
Society of Brest, France
Ventspils
Belarus-Folk-Music
Vitebsk
Hiking in Latvia
Molchat-Doma-Discoteque-Official-Music-Video-Молчат-Дома-Дискотека
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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Calvaire de la Chapelle Notre-Dame de Trévarn17.8 km away
  • 📍
    Calvaire de Saint-Servais27.9 km away
  • 📍
    Calvaire de la chapelle de Saint-They40.3 km away
  • 📍
    Carhaix68.6 km away
  • 📍
    Carhaix-Plouguer68.6 km away

Network Profile

Overall Strength
i
1.16% of network
(80.86B)
Strength Breakdown
  • This Post (1.16%)
  • Vitebsk (2.62%)
  • Grodno (2.33%)
  • Minsk (2.33%)
  • Gomel (1.74%)
  • Mogilev (1.16%)
  • Jēkabpils (1.16%)
  • Jūrmala (1.16%)
  • Rēzekne (1.16%)
  • Ventspils (0.58%)
  • Daugavpils (0.29%)
  • Liepāja (0.29%)
  • Riga (0.29%)
  • Valmiera (0.29%)
  • Ruba (0.29%)
Dominant nodes (excluded from chart)
Latvia 69.19%Belarus 13.95%
Influence Score
i
4.89% of network
(10.93)
Influence Breakdown
  • This Post (4.89%)
  • Grodno (9.77%)
  • Minsk (9.77%)
  • Ventspils (9.77%)
  • Gomel (7.33%)
  • Latvia (5.93%)
  • Mogilev (4.89%)
  • Vitebsk (4.89%)
  • Daugavpils (4.89%)
  • Jēkabpils (4.89%)
  • Jūrmala (4.89%)
  • Liepāja (4.89%)
  • Rēzekne (4.89%)
  • Riga (4.89%)
  • Valmiera (4.89%)
  • Ruba (4.89%)
  • Belarus (3.66%)
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 *
                           ( 8 [Grodno] *
                            8 [Minsk] *
                            2 [Ventspils] *
                            6 [Gomel] *
                            238 [Latvia] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            1 [Daugavpils] *
                            4 [Jēkabpils] *
                            4 [Jūrmala] *
                            1 [Liepāja] *
                            4 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ruba] *
                            48 [Belarus]
                           )

                         = 80.86B

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

                         = 10.93
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)
1.16% (80.86B overall)
  • This Post (1.16%)
  • Vitebsk (2.62%)
  • Grodno (2.33%)
  • Minsk (2.33%)
  • Gomel (1.74%)
  • Mogilev (1.16%)
  • Jēkabpils (1.16%)
  • Jūrmala (1.16%)
  • Rēzekne (1.16%)
  • Ventspils (0.58%)
  • Daugavpils (0.29%)
  • Liepāja (0.29%)
  • Riga (0.29%)
  • Valmiera (0.29%)
  • Ruba (0.29%)
Dominant nodes (excluded from chart)
Latvia 69.19%Belarus 13.95%
Influence Share (vs Direct Neighbours)
4.89% (10.93 overall)
  • This Post (4.89%)
  • Grodno (9.77%)
  • Minsk (9.77%)
  • Ventspils (9.77%)
  • Gomel (7.33%)
  • Latvia (5.93%)
  • Mogilev (4.89%)
  • Vitebsk (4.89%)
  • Daugavpils (4.89%)
  • Jēkabpils (4.89%)
  • Jūrmala (4.89%)
  • Liepāja (4.89%)
  • Rēzekne (4.89%)
  • Riga (4.89%)
  • Valmiera (4.89%)
  • Ruba (4.89%)
  • Belarus (3.66%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Grodno ↗
Str: 8Inf: 2
Minsk ↗
Str: 8Inf: 2
Ventspils ↗
Str: 2Inf: 2
Gomel ↗
Str: 6Inf: 1.5
Latvia ↗
Str: 238Inf: 1.21
Mogilev ↗
Str: 4Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Jūrmala ↗
Str: 4Inf: 1
Liepāja ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 4Inf: 1
Riga ↗
Str: 1Inf: 1
Valmiera ↗
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
Valmiera ↗
Str: 1Inf: 1
Riga ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 4Inf: 1
Liepāja ↗
Str: 1Inf: 1
Jūrmala ↗
Str: 4Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Mogilev ↗
Str: 4Inf: 1
Latvia ↗
Str: 238Inf: 1.21
Gomel ↗
Str: 6Inf: 1.5
Ventspils ↗
Str: 2Inf: 2
Minsk ↗
Str: 8Inf: 2
Grodno ↗
Str: 8Inf: 2

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

Outbound Tags (2)
Belarus
Latvia
Inbound Posts (2)
Latvia
Belarus
Last calculated: Jul 25, 10:47 PM
Brest, Finistère, France41

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