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Daugavpils

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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • Thumbnail for Latvia
    Latvia0.0 km away
  • Thumbnail for Rēzekne
    Rēzekne0.0 km away
  • Thumbnail for Ventspils
    Ventspils0.0 km away
  • Thumbnail for Jūrmala
    Jūrmala0.0 km away
  • Thumbnail for Minsk
    Minsk0.0 km away

Network Profile

Overall Strength
i
0.36% of network
(13.16M)
Strength Breakdown
  • This Post (0.36%)
  • Vitebsk (3.21%)
  • Minsk (2.14%)
  • Brest (1.43%)
  • Gomel (1.43%)
  • Grodno (1.43%)
  • Mogilev (1.43%)
  • Jēkabpils (1.43%)
  • Jūrmala (0.36%)
  • Liepāja (0.36%)
  • Rēzekne (0.36%)
  • Riga (0.36%)
  • Valmiera (0.36%)
  • Ventspils (0.36%)
Dominant nodes (excluded from chart)
Latvia 85.00%
Influence Score
i
6.36% of network
(1.82)
Influence Breakdown
  • This Post (6.36%)
  • Minsk (9.55%)
  • Latvia (7.73%)
  • Brest (6.36%)
  • Gomel (6.36%)
  • Grodno (6.36%)
  • Mogilev (6.36%)
  • Vitebsk (6.36%)
  • Jēkabpils (6.36%)
  • Jūrmala (6.36%)
  • Liepāja (6.36%)
  • Rēzekne (6.36%)
  • Riga (6.36%)
  • Valmiera (6.36%)
  • Ventspils (6.36%)
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 14 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 1 *
                           ( 6 [Minsk] *
                            238 [Latvia] *
                            4 [Brest] *
                            4 [Gomel] *
                            4 [Grodno] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            4 [Jēkabpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ventspils]
                           )

                         = 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 [Jēkabpils] *
                            1 [Jūrmala] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Ventspils]
                           )

                         = 1.82
Outbound 1 Tags on post
Inbound 1 Posts tagging this
Connections 14 Total nodes
Base Node Strength 1
Base Node Influence 1
Strength Share (vs Direct Neighbours)
0.36% (13.16M overall)
  • This Post (0.36%)
  • Vitebsk (3.21%)
  • Minsk (2.14%)
  • Brest (1.43%)
  • Gomel (1.43%)
  • Grodno (1.43%)
  • Mogilev (1.43%)
  • Jēkabpils (1.43%)
  • Jūrmala (0.36%)
  • Liepāja (0.36%)
  • Rēzekne (0.36%)
  • Riga (0.36%)
  • Valmiera (0.36%)
  • Ventspils (0.36%)
Dominant nodes (excluded from chart)
Latvia 85.00%
Influence Share (vs Direct Neighbours)
6.36% (1.82 overall)
  • This Post (6.36%)
  • Minsk (9.55%)
  • Latvia (7.73%)
  • Brest (6.36%)
  • Gomel (6.36%)
  • Grodno (6.36%)
  • Mogilev (6.36%)
  • Vitebsk (6.36%)
  • Jēkabpils (6.36%)
  • Jūrmala (6.36%)
  • Liepāja (6.36%)
  • Rēzekne (6.36%)
  • Riga (6.36%)
  • Valmiera (6.36%)
  • Ventspils (6.36%)

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
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
Weakest Connections (Lowest Multipliers)
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
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 (1)
Latvia
Inbound Posts (1)
Latvia
Last calculated: Oct 1, 4:52 AM
Vitsebsk Region, Belarus47

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  • Brest
  • Liepāja
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  • Grodno
  • Riga
  • Minsk
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