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Jēkabpils

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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Blakai61.8 km away
  • Thumbnail for LVA
    LVA76.9 km away
  • 📍
    Dagdas ala110.8 km away
  • Thumbnail for Utena
    Utena112.1 km away
  • 📍
    Las117.4 km away

Network Profile

Overall Strength
i
1.42% of network
(13.16M)
Strength Breakdown
  • This Post (1.42%)
  • Vitebsk (3.20%)
  • Minsk (2.14%)
  • Brest (1.42%)
  • Gomel (1.42%)
  • Grodno (1.42%)
  • Mogilev (1.42%)
  • Daugavpils (0.36%)
  • Jūrmala (0.36%)
  • Liepāja (0.36%)
  • Rēzekne (0.36%)
  • Riga (0.36%)
  • Valmiera (0.36%)
  • Ventspils (0.36%)
  • Symbols of Jēkabpils (0.36%)
Dominant nodes (excluded from chart)
Latvia 84.70%
Influence Score
i
5.98% of network
(1.82)
Influence Breakdown
  • This Post (5.98%)
  • Minsk (8.97%)
  • Latvia (7.27%)
  • Brest (5.98%)
  • Gomel (5.98%)
  • Grodno (5.98%)
  • Mogilev (5.98%)
  • Vitebsk (5.98%)
  • Daugavpils (5.98%)
  • Jūrmala (5.98%)
  • Liepāja (5.98%)
  • Rēzekne (5.98%)
  • Riga (5.98%)
  • Valmiera (5.98%)
  • Ventspils (5.98%)
  • Symbols of Jēkabpils (5.98%)
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 15 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 6 [Minsk] *
                            238 [Latvia] *
                            4 [Brest] *
                            4 [Gomel] *
                            4 [Grodno] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            1 [Daugavpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ventspils] *
                            1 [Symbols of Jēkabpils]
                           )

                         = 13.16M

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

                         = 1.82
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 15 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
1.42% (13.16M overall)
  • This Post (1.42%)
  • Vitebsk (3.20%)
  • Minsk (2.14%)
  • Brest (1.42%)
  • Gomel (1.42%)
  • Grodno (1.42%)
  • Mogilev (1.42%)
  • Daugavpils (0.36%)
  • Jūrmala (0.36%)
  • Liepāja (0.36%)
  • Rēzekne (0.36%)
  • Riga (0.36%)
  • Valmiera (0.36%)
  • Ventspils (0.36%)
  • Symbols of Jēkabpils (0.36%)
Dominant nodes (excluded from chart)
Latvia 84.70%
Influence Share (vs Direct Neighbours)
5.98% (1.82 overall)
  • This Post (5.98%)
  • Minsk (8.97%)
  • Latvia (7.27%)
  • Brest (5.98%)
  • Gomel (5.98%)
  • Grodno (5.98%)
  • Mogilev (5.98%)
  • Vitebsk (5.98%)
  • Daugavpils (5.98%)
  • Jūrmala (5.98%)
  • Liepāja (5.98%)
  • Rēzekne (5.98%)
  • Riga (5.98%)
  • Valmiera (5.98%)
  • Ventspils (5.98%)
  • Symbols of Jēkabpils (5.98%)

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
Gomel ↗
Str: 4Inf: 1
Grodno ↗
Str: 4Inf: 1
Mogilev ↗
Str: 4Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Daugavpils ↗
Str: 1Inf: 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
Symbols of Jēkabpils ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
Symbols of Jēkabpils ↗
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
Daugavpils ↗
Str: 1Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Mogilev ↗
Str: 4Inf: 1
Grodno ↗
Str: 4Inf: 1
Gomel ↗
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)
Latvia
Symbols of Jēkabpils
Inbound Posts (2)
Latvia
Symbols of Jēkabpils
Last calculated: Sep 30, 12:53 PM
50

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