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Brest

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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
1.02% of network
(3.71T)
Strength Breakdown
  • This Post (1.02%)
  • Alexander Lukashenko (8.88%)
  • Vitebsk (2.28%)
  • Grodno (2.03%)
  • Minsk (2.03%)
  • Gomel (1.52%)
  • Mogilev (1.02%)
  • Jēkabpils (1.02%)
  • Jūrmala (1.02%)
  • Rēzekne (1.02%)
  • Ventspils (0.51%)
  • Daugavpils (0.25%)
  • Liepāja (0.25%)
  • Riga (0.25%)
  • Valmiera (0.25%)
  • Ruba (0.25%)
Dominant nodes (excluded from chart)
Latvia 60.41%Belarus 15.99%
Influence Score
i
4.57% of network
(15.87)
Influence Breakdown
  • This Post (4.57%)
  • Grodno (9.14%)
  • Minsk (9.14%)
  • Ventspils (9.14%)
  • Gomel (6.85%)
  • Alexander Lukashenko (6.40%)
  • Latvia (5.55%)
  • Mogilev (4.57%)
  • Vitebsk (4.57%)
  • Daugavpils (4.57%)
  • Jēkabpils (4.57%)
  • Jūrmala (4.57%)
  • Liepāja (4.57%)
  • Rēzekne (4.57%)
  • Riga (4.57%)
  • Valmiera (4.57%)
  • Ruba (4.57%)
  • Belarus (3.55%)
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 17 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 8 [Grodno] *
                            8 [Minsk] *
                            2 [Ventspils] *
                            6 [Gomel] *
                            35 [Alexander Lukashenko] *
                            238 [Latvia] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            1 [Daugavpils] *
                            4 [Jēkabpils] *
                            4 [Jūrmala] *
                            1 [Liepāja] *
                            4 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ruba] *
                            63 [Belarus]
                           )

                         = 3.71T

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 2 [Grodno] *
                            2 [Minsk] *
                            2 [Ventspils] *
                            1.5 [Gomel] *
                            1.4 [Alexander Lukashenko] *
                            1.21 [Latvia] *
                            1 [Mogilev] *
                            1 [Vitebsk] *
                            1 [Daugavpils] *
                            1 [Jēkabpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ruba] *
                            0.7778 [Belarus]
                           )

                         = 15.87
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 17 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.02% (3.71T overall)
  • This Post (1.02%)
  • Alexander Lukashenko (8.88%)
  • Vitebsk (2.28%)
  • Grodno (2.03%)
  • Minsk (2.03%)
  • Gomel (1.52%)
  • Mogilev (1.02%)
  • Jēkabpils (1.02%)
  • Jūrmala (1.02%)
  • Rēzekne (1.02%)
  • Ventspils (0.51%)
  • Daugavpils (0.25%)
  • Liepāja (0.25%)
  • Riga (0.25%)
  • Valmiera (0.25%)
  • Ruba (0.25%)
Dominant nodes (excluded from chart)
Latvia 60.41%Belarus 15.99%
Influence Share (vs Direct Neighbours)
4.57% (15.87 overall)
  • This Post (4.57%)
  • Grodno (9.14%)
  • Minsk (9.14%)
  • Ventspils (9.14%)
  • Gomel (6.85%)
  • Alexander Lukashenko (6.40%)
  • Latvia (5.55%)
  • Mogilev (4.57%)
  • Vitebsk (4.57%)
  • Daugavpils (4.57%)
  • Jēkabpils (4.57%)
  • Jūrmala (4.57%)
  • Liepāja (4.57%)
  • Rēzekne (4.57%)
  • Riga (4.57%)
  • Valmiera (4.57%)
  • Ruba (4.57%)
  • Belarus (3.55%)

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
Alexander Lukashenko ↗
Str: 35Inf: 1.4
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: 63Inf: 0.7778
Weakest Connections (Lowest Multipliers)
Belarus ↗
Str: 63Inf: 0.7778
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
Alexander Lukashenko ↗
Str: 35Inf: 1.4
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: Jun 11, 7:54 PM
Brest, Finistère, France39

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