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Stockholm School of Economics

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Analyzing Network Connections...

Loading Map...

Nearest Locations

  • 📍
    Stockholm School of Economics, Main building0.0 km away
  • 📍
    LM Ericssons byggnader Tulegatan0.2 km away
  • Thumbnail for Estonian House in Stockholm
    Estonian House in Stockholm0.6 km away
  • 📍
    Scalateatern0.6 km away
  • 📍
    Davidsons paviljonger0.6 km away

Network Profile

Overall Strength
i
22.22% of network
(144)
Strength Breakdown
  • This Post (22.22%)
  • main building (50.00%)
  • Stockholm School of Economics, Main building (22.22%)
  • Rubaba Dowla (5.56%)
Influence Score
i
25.00% of network
(1)
Influence Breakdown
  • This Post (25.00%)
  • Rubaba Dowla (25.00%)
  • Stockholm School of Economics, Main building (25.00%)
  • main building (25.00%)
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 3 direct neighbours)
Network_Strength (CV) = Base_PV * (Neighbour_1_PV * Neighbour_2_PV * ...)
                         = 4 *
                           ( 1 [Rubaba Dowla] *
                            4 [Stockholm School of Economics, Main building] *
                            9 [main building]
                           )

                         = 144

Network_Influence (TV) = Base_IV * (Neighbour_1_IV * Neighbour_2_IV * ...)
                         = 1 *
                           ( 1 [Rubaba Dowla] *
                            1 [Stockholm School of Economics, Main building] *
                            1 [main building]
                           )

                         = 1
Outbound 2 Tags on post
Inbound 2 Posts tagging this
Connections 3 Total nodes
Base Node Strength 4
Base Node Influence 1
Strength Share (vs Direct Neighbours)
22.22% (144 overall)
  • This Post (22.22%)
  • main building (50.00%)
  • Stockholm School of Economics, Main building (22.22%)
  • Rubaba Dowla (5.56%)
Influence Share (vs Direct Neighbours)
25.00% (1 overall)
  • This Post (25.00%)
  • Rubaba Dowla (25.00%)
  • Stockholm School of Economics, Main building (25.00%)
  • main building (25.00%)

Connected Network Hierarchy

Sort list by:
Top Network Boosters (Highest Multipliers)
Rubaba Dowla ↗
Str: 1Inf: 1
Stockholm School of Economics, Main building ↗
Str: 4Inf: 1
main building ↗
Str: 9Inf: 1
Weakest Connections (Lowest Multipliers)
main building ↗
Str: 9Inf: 1
Stockholm School of Economics, Main building ↗
Str: 4Inf: 1
Rubaba Dowla ↗
Str: 1Inf: 1

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

Outbound Tags (2)
Rubaba Dowla
Stockholm School of Economics, Main building
Inbound Posts (2)
Rubaba Dowla
Stockholm School of Economics, Main building
Last calculated: Oct 19, 11:22 PM
Stockholm, Stockholm County, Sweden26

Related Content

Figures

  • Rubaba Dowla

Topics

  • Stockholm School of Economics, Main building
  • main building

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