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Jūrmala

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Related Images

Gomel
Jēkabpils
Architecture of Jūrmala
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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Blackheads18.9 km away
  • 📍
    United Baltic Duchy18.9 km away
  • Thumbnail for Riga
    Riga19.0 km away
  • 📍
    Las20.0 km away
  • Thumbnail for Jelgava
    Jelgava35.9 km away

Network Profile

Overall Strength
i
1.35% of network
(1.68B)
Strength Breakdown
  • This Post (1.35%)
  • Vitebsk (3.04%)
  • Grodno (2.70%)
  • Minsk (2.70%)
  • Gomel (2.03%)
  • Brest (1.35%)
  • Mogilev (1.35%)
  • Jēkabpils (1.35%)
  • Rēzekne (1.35%)
  • Ventspils (0.68%)
  • Daugavpils (0.34%)
  • Liepāja (0.34%)
  • Riga (0.34%)
  • Valmiera (0.34%)
  • Education in Jūrmala (0.34%)
Dominant nodes (excluded from chart)
Latvia 80.41%
Influence Score
i
5.07% of network
(14.57)
Influence Breakdown
  • This Post (5.07%)
  • Grodno (10.14%)
  • Minsk (10.14%)
  • Ventspils (10.14%)
  • Gomel (7.61%)
  • Latvia (6.16%)
  • Brest (5.07%)
  • Mogilev (5.07%)
  • Vitebsk (5.07%)
  • Daugavpils (5.07%)
  • Jēkabpils (5.07%)
  • Liepāja (5.07%)
  • Rēzekne (5.07%)
  • Riga (5.07%)
  • Valmiera (5.07%)
  • Education in Jūrmala (5.07%)
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 *
                           ( 8 [Grodno] *
                            8 [Minsk] *
                            2 [Ventspils] *
                            6 [Gomel] *
                            238 [Latvia] *
                            4 [Brest] *
                            4 [Mogilev] *
                            9 [Vitebsk] *
                            1 [Daugavpils] *
                            4 [Jēkabpils] *
                            1 [Liepāja] *
                            4 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Education in Jūrmala]
                           )

                         = 1.68B

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 [Brest] *
                            1 [Mogilev] *
                            1 [Vitebsk] *
                            1 [Daugavpils] *
                            1 [Jēkabpils] *
                            1 [Liepāja] *
                            1 [Rēzekne] *
                            1 [Riga] *
                            1 [Valmiera] *
                            1 [Education in Jūrmala]
                           )

                         = 14.57
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)
1.35% (1.68B overall)
  • This Post (1.35%)
  • Vitebsk (3.04%)
  • Grodno (2.70%)
  • Minsk (2.70%)
  • Gomel (2.03%)
  • Brest (1.35%)
  • Mogilev (1.35%)
  • Jēkabpils (1.35%)
  • Rēzekne (1.35%)
  • Ventspils (0.68%)
  • Daugavpils (0.34%)
  • Liepāja (0.34%)
  • Riga (0.34%)
  • Valmiera (0.34%)
  • Education in Jūrmala (0.34%)
Dominant nodes (excluded from chart)
Latvia 80.41%
Influence Share (vs Direct Neighbours)
5.07% (14.57 overall)
  • This Post (5.07%)
  • Grodno (10.14%)
  • Minsk (10.14%)
  • Ventspils (10.14%)
  • Gomel (7.61%)
  • Latvia (6.16%)
  • Brest (5.07%)
  • Mogilev (5.07%)
  • Vitebsk (5.07%)
  • Daugavpils (5.07%)
  • Jēkabpils (5.07%)
  • Liepāja (5.07%)
  • Rēzekne (5.07%)
  • Riga (5.07%)
  • Valmiera (5.07%)
  • Education in Jūrmala (5.07%)

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
Brest ↗
Str: 4Inf: 1
Mogilev ↗
Str: 4Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Liepāja ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 4Inf: 1
Riga ↗
Str: 1Inf: 1
Valmiera ↗
Str: 1Inf: 1
Education in Jūrmala ↗
Str: 1Inf: 1
Weakest Connections (Lowest Multipliers)
Education in Jūrmala ↗
Str: 1Inf: 1
Valmiera ↗
Str: 1Inf: 1
Riga ↗
Str: 1Inf: 1
Rēzekne ↗
Str: 4Inf: 1
Liepāja ↗
Str: 1Inf: 1
Jēkabpils ↗
Str: 4Inf: 1
Daugavpils ↗
Str: 1Inf: 1
Vitebsk ↗
Str: 9Inf: 1
Mogilev ↗
Str: 4Inf: 1
Brest ↗
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)
Education in Jūrmala
Latvia
Inbound Posts (2)
Latvia
Education in Jūrmala
Last calculated: Jul 3, 3:04 AM
39

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  • Education in Jūrmala